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The Oxford Handbook of AI Governance
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... Large Language Models (LLMs) have revolutionized natural language processing, demonstrating remarkable capabilities in various tasks (OpenAI, 2022;Touvron et al., 2023a;b;Song et al., 2024;Zhang et al., 2024a), but their increasing power and ubiquity have raised critical challenges in ensuring safety and alignment with human values (Shayegani et al., 2023;Das et al., 2024;Chowdhury et al., 2024). The potential for LLMs to generate harmful, biased, or sensitive content poses significant risks to individuals, organizations, and society at scale (Chao et al., 2023;Zou et al., 2023b;Mehrotra et al., 2023;Wei et al., 2024;. ...
... As contemporary defense strategies that solely supervise model outputs often fall short in achieving the necessary levels of controllability and reliability, there has been a growing interest in techniques that analyze and manage the internal representations of models. Representation engineering encompasses a broad range of research areas, including the discovery of emergent, interpretable structures within intermediate representations (Caron et al., 2021;Mikolov et al., 2013;Zou et al., 2023a), the identification and modification of embedded knowledge (Meng et al., 2022a;b;Mitchell et al., 2021), and the steering of model outputs (Bau et al., 2020;Ilharco et al., 2022;Ling et al., 2021;Upchurch et al., 2017;Turner et al., 2023). A particularly relevant advancement in this field is the control vector baseline introduced by Zou et al. (2023a), which enhances large language models' resilience against adversarial attacks. ...
... Model Editing and Tuning Model editing is an effective approach for knowledge editing (KE), where the internal structure of the model is adjusted to alter its output for specific edited content. Recent model editing and tuning techniques for LLMs (Meng et al., 2022a;b;Yao et al., 2023;Bi et al., 2024c) commonly involve either integrating an auxiliary network with the original model or modifying and adding parameters to steer the model's responses. In-Context Editing (ICE) (Bi et al., 2024e;a;b; and In-Context Understanding show promise by allowing edits to LLMs through prompting with modified facts and retrieving relevant editing demonstrations from a memory of edits. ...
As Large Language Models (LLMs) grow increasingly powerful, ensuring their safety and alignment with human values remains a critical challenge. Ideally, LLMs should provide informative responses while avoiding the disclosure of harmful or sensitive information. However, current alignment approaches, which rely heavily on refusal strategies, such as training models to completely reject harmful prompts or applying coarse filters are limited by their binary nature. These methods either fully deny access to information or grant it without sufficient nuance, leading to overly cautious responses or failures to detect subtle harmful content. For example, LLMs may refuse to provide basic, public information about medication due to misuse concerns. Moreover, these refusal-based methods struggle to handle mixed-content scenarios and lack the ability to adapt to context-dependent sensitivities, which can result in over-censorship of benign content. To overcome these challenges, we introduce HiddenGuard, a novel framework for fine-grained, safe generation in LLMs. HiddenGuard incorporates Prism (rePresentation Router for In-Stream Moderation), which operates alongside the LLM to enable real-time, token-level detection and redaction of harmful content by leveraging intermediate hidden states. This fine-grained approach allows for more nuanced, context-aware moderation, enabling the model to generate informative responses while selectively redacting or replacing sensitive information, rather than outright refusal. We also contribute a comprehensive dataset with token-level fine-grained annotations of potentially harmful information across diverse contexts. Our experiments demonstrate that HiddenGuard achieves over 90% in F1 score for detecting and redacting harmful content while preserving the overall utility and informativeness of the model's responses.
... Corporate governance, as a practice, extends back to the formation of the major chartered companies in the 16th and 17th centuries (Wells, 2010). However, it was only in the mid-1970s that corporate governance emerged as a term in the United States, and it was not until the mid-1990s that it started becoming a buzzword and a well-established regulatory and market issue in many countries across the globe (Cheffins, 2013); inevitably, corporate governance has attracted significant academic interest. ...
... In the decades that followed, the issues that became the epicenter of analysis included ownership structures and their impact on the protection of minority investors from expropriation by managers or controlling shareholders (La Porta et al., 2000Porta et al., , 1999Porta et al., , 1998Porta et al., , 1997. The primary research focus consisted of regulatory mechanisms including statutory rules (hard law, such as the Sarbanes Oxley Act in the US) and standards of best practice (soft law, such as the Cadbury recommendations of good governance in the UK) aimed at addressing common governance problems (Cheffins, 2013). ...
... Hence, the effectiveness of enforcement mechanisms was also brought to the fore (La Porta et al., 1998). This branch of the literature constitutes the backbone of an influential and well-established approach, known as law and economics (Aguilera et al., 2013;Cheffins, 2013;Goyer, 2010). This perspective has also been effectively enriched by comparative studies which provided significant insights into the effects of the globalization of regulatory settings, reforms and enforcement processes across economies (Zattoni et al., 2020). ...
... On the other hand, as the use of personalisation strategies that are driven by artificial intelligence in digital marketing continues to grow and attract all the attentions from individuals and societies, it is very necessary to do an in-depth analysis of the ethical issues that are linked with this approach [5]. Concerns and issues have been raised about the ethical use of AI, potential risks and hazards, and the need of suitable safeguards to protect the rights and well-being of consumers, despite the fact that AI has a vast amount of promise [6,7]. Scholars have emphasized the importance of addressing global AI ethics and the global governance of AI to ensure responsible and ethical use of AI technologies [7,8]. ...
... Concerns and issues have been raised about the ethical use of AI, potential risks and hazards, and the need of suitable safeguards to protect the rights and well-being of consumers, despite the fact that AI has a vast amount of promise [6,7]. Scholars have emphasized the importance of addressing global AI ethics and the global governance of AI to ensure responsible and ethical use of AI technologies [7,8]. ...
Artificial intelligence (AI) and machine learning have revolutionized digital marketing by enabling highly personalized experiences for consumers. While AI-driven personalization presents opportunities to improve engagement and loyalty, its widespread use also gives rise to ethical challenges regarding privacy, bias, manipulation, and societal impacts. This study examines these ethical considerations through a comprehensive analysis of literature and case studies. An updated classification of key issues is proposed, including privacy risks from vast data collection, algorithmic bias perpetuating discrimination, potential for consumer manipulation, economic disruption, and lack of transparency impeding accountability. Recommendations are suggested to help ensure AI-powered personalization respects human values, avoids unfair outcomes, and enhances well-being.
... It is suggested that horizontal public governance and policy network concepts should be used to shift from vertical to horizontal network management. Nevertheless, caution should be exercised, since new forms of dominance may be hidden (Chhotray et al, 2009;Klijn & Koppenjan, 2015;Rhodes, 2012). ...
... According to Dowding (1995), many previous studies have examined policy networks to explain only the relationship between the state and social actors (Rhodes, 2012). Marsh & Smith (2000) developed a dialectical approach to policy networks in order to provide a more concise explanation of policy change. ...
Objective: The study examines how policy implementation, policy network management, and capacity building can help promote community enterprises in Thailand. Theoretical Framework: A theoretical approach has been used in this study. Concepts such as Small and Micro Community Enterprise Policies, Policy Networks, Policy Implementation, and Community Enterprise Management Potential have been considered. Method: This qualitative study examined how policy implementation, policy network management, and capacity building can help promote community enterprises in Thailand. Several government agencies and community enterprises were interviewed for the research. While community enterprise promotion policies are widely supported, there are challenges in tailoring them to local contexts and ensuring effective collaboration among stakeholders. Bottom-up and context-sensitive policymaking is needed, as well as stronger interorganizational coordination and data sharing mechanisms. Results and Discussion: The research highlights the importance of tailoring policies to local contexts, fostering collaboration among stakeholders, and addressing capacity building needs in a comprehensive manner to promote the success of community enterprises in Thailand. The findings offer guidance for policymakers and practitioners while also suggesting areas for further research to deepen understanding and inform future interventions. Research Implications: This study contributes to a more nuanced understanding of what it takes to promote community enterprises in Thailand and offers valuable guidance for policymakers and practitioners working to support community-based entrepreneurship. The findings underscore the importance of context-sensitive policies, strong collaboration among stakeholders, and a holistic approach to capacity building. Originality/Value: The study contributes to a nuanced understanding of what shapes community enterprise success in Thailand. This report offers policymakers and practitioners valuable insights into creating an enabling environment for community-based entrepreneurship.
... Governance will play an essential role in the safety and alignment of LLMs, in particular, and AI in general (Bullock et al., 2022a). Governance encompasses not only formal regulations, but also a number of other mechanisms including norms, soft law, codes of ethics, co-regulation, industry standards, and sector-specific guidelines (Veale et al., 2023); see Table 5. Governance could supplement technical solutions, e.g. by mandating they be applied as appropriate. ...
This work identifies 18 foundational challenges in assuring the alignment and safety of large language models (LLMs). These challenges are organized into three different categories: scientific understanding of LLMs, development and deployment methods, and sociotechnical challenges. Based on the identified challenges, we pose 200+ concrete research questions.
... This concept of provisioning a text-based set of rules to be narrowly interpreted by an AI agent for training and selfevaluation emerged as an AI safety approach in relation to LLMs; which are a subset of AI agent created from Natural Language Processing techniques (Bai et al., 2022;Durmus et al., 2023). AI governance refers to the ecosystem of norms, markets, and institutions that shape how AI is built and deployed, as well as the policy and research required to maintain it, in line with human interests (Bullock et al., 2024;Dafoe, 2024). However, the constitutional approach to AI governance is limiting, as it draws on understandings of a constitution as a closed system of norms or unifying ideals that are expressed in plain language and employed as a governance mechanism. ...
This study focuses on the practicalities of establishing and maintaining AI infrastructure, as well as the considerations for responsible governance by investigating the integration of a pre-trained large language model (LLM) with an organisation’s knowledge management system via a chat interface. The research adopts the concept of “AI as a constituted system” to emphasise the social, technical, and institutional factors that contribute to AI’s governance and accountability. Through an ethnographic approach, this article details the iterative processes of negotiation, decision-making, and reflection among organisational stakeholders as they develop, implement, and manage the AI system. The findings indicate that LLMs can be effectively governed and held accountable to stakeholder interests within specific contexts, specifically, when clear institutional boundaries facilitate innovation while navigating the risks related to data privacy and AI misbehaviour. Effective constitution and use can be attributed to distinct policy creation processes to guide AI’s operation, clear lines of responsibility, and localised feedback loops to ensure accountability for actions taken. This research provides a foundational perspective to better understand algorithmic accountability and governance within organisational contexts. It also envisions a future where AI is not universally scaled but consists of localised, customised LLMs tailored to stakeholder interests.
... The account-holder can kindly ask the account-giver to please follow a specific course of action because this is reasonable and justified or she can forcefully state that it is absolutely imperative that the accountgiver complies. A consensual style of account-holding aligns with the ideal of giving voice while a confrontational style is more aligned to a corrective strategy, which has for instance been described in literatures on participation (Ron, 2012). Confrontationally demanding a specific decision to be made may serve as a form of extrinsic motivation in the same vein as an accountability demand in a formal setting would. ...
Punitive measures (sanctions) are central to accountability. Their use is however costly as they harm relationships. Prior research shows that punitive measures often remain unused. Public sector actors further operate in informal accountability settings where punitive measures are absent. Additionally, doctrines such as New Public Governance prioritize informal networks above hierarchy and punitive measures. Against this background, we study when and why nonpunitive accountability can be effective. Three theoretical logics are developed for decision‐making behavior under the condition of accountability. We theorize account‐givers are driven by a combination of extrinsic, intrinsic, and relational motivation. A conjoint experiment is used to study decisions (N = 761) of administrative leaders in Denmark in varying nonpunitive accountability conditions. Our findings suggest that a combination of extrinsic motivation and relational motivation explains decisions of account‐givers in nonpunitive settings. The study expands our theoretical knowledge of the behavioral effects of accountability and offers insights for policy practitioners.
... There are at least two distinct arguments for using such stochastic treatment rules: an ethical, and an epistemic one. The ethical argument is that hard cut-offs are unfair because two people on opposite sides of the threshold are treated very differently (Vredenburgh, 2022). The epistemic argument is that we often do not have much data for some covariates, and if we then never assign some treatments to them, we will never gain that information. ...
The most common approach to causal modelling is the potential outcomes framework due to Neyman and Rubin. In this framework, outcomes of counterfactual treatments are assumed to be well-defined. This metaphysical assumption is often thought to be problematic yet indispensable. The conventional approach relies not only on counterfactuals, but also on abstract notions of distributions and assumptions of independence that are not directly testable. In this paper, we construe causal inference as treatment-wise predictions for finite populations where all assumptions are testable; this means that one can not only test predictions themselves (without any fundamental problem), but also investigate sources of error when they fail. The new framework highlights the model-dependence of causal claims as well as the difference between statistical and scientific inference.
... Strengthening or deepening citizens' participation in public governance and policy development has the potential to help political processes generate legitimacy and fairness (Pateman, 1970). As a democratic reconstruction strategy, Ansell (2012) claims that by deepening participation and deliberation, collaborative modes of governance seek to expand democratic control and restore trust in government. Some describe interactive policymaking as an injection of direct democracyor citizen participationinto a decaying system of representation (Edelenbos, van Schie and Gerrits, 2010;Røiseland and Vabo, 2016). ...
How can elected representatives' attitude to citizen participation in policymaking function as a driver of, and barrier to, democratic innovation? This paper aims to answer this question and empirically assess local councillors' views on innovative efforts to enhance citizens' participation in interactive governance processes in Norway. Unlike the bulk of previous research, which has focussed on the potential impact of citizen participation, this paper contributes to the understanding of what politicians think of the innovative measures. With councillors acting as gatekeepers with respect to democratic innovation, investigating their attitudes can help clarify why democratic renewal is or is not being prioritised. The findings of this empirical study on attempts to introduce new participatory initiatives in four Norwegian municipalities indicate that local politicians see an urgent need to innovate in order to increase public problem-solving capacity and efficiency; however, they are less concerned about strengthening democracy in and of itself. In addition, established democratic structures prevent elected representatives from seeing it as desirable or possible to involve citizens more directly in policymaking-even when they acknowledge that there are good reasons for doing so. Furthermore, councillor's attitudes may more generally affect democratic innovation at the local level of government since politicians decide whether and how to promote innovations in a local representative democracy.
... A emergência dos estudos sobre governança (1980-90) é relacionada a mudanças na atuação do Estado (com a retração e a transferência de funções executoras, a ampliação de função regulatória e a participação da sociedade civil); ao aumento da complexidade dos problemas de políticas e das políticas públicas em si; e à multiplicação de níveis e escalas espaciais envolvidos em funções de responsabilidade de governos (Bevir, 2011;Rhodes, 2012). ...
Este artigo tem como objetivo analisar os arranjos de governança e a participação social em um Projeto de Intervenção Urbana (PIU), instrumento urbanístico previsto no Plano Diretor de São Paulo (SP) de 2014, e seu impacto em territórios populares. Apresenta-se neste texto o estudo de caso de duas Zonas Especiais de Interesse Social (ZEIS) situadas no perímetro do PIU Arco Jurubatuba, na zona sul da cidade. As questões que orientaram o trabalho foram: (i) como se deu a participação das comunidades desses territórios populares nas discussões sobre o projeto de intervenção que as afetaria; e (ii) qual o arranjo de governança resultante da proposta, considerando a sobreposição de instrumentos dentro do PIU. Evidencia-se que os arranjos institucionais e os instrumentos selecionados para elaboração e futura implementação do PIU aumentam a complexidade do projeto, a fragmentação do processo e a superficialidade dos procedimentos de participação e legitimação da intervenção. Escolhas feitas durante a elaboração do PIU e a matrioska de instrumentos resultante dificultam a identificação do momento e do lócus de tomada de decisões cruciais sobre a intervenção que atinge os territórios populares, prejudicando a participação social e adiando (ou impedindo) contestações.
... A emergência dos estudos sobre governança (1980-90) é relacionada a mudanças na atuação do Estado (com a retração e a transferência de funções executoras, a ampliação de função regulatória e a participação da sociedade civil); ao aumento da complexidade dos problemas de políticas e das políticas públicas em si; e à multiplicação de níveis e escalas espaciais envolvidos em funções de responsabilidade de governos (Bevir, 2011;Rhodes, 2012). ...
The aim of this article is to analyze the arrangements for the governance and social participation in the Arco Jurubatuba Urban Intervention Project (UIP), an urban instrument provided for in the 2014 São Paulo (SP) Master Plan, and its impacts on low-income territories. The article presents a case study of two Special Zones of Social Interest (ZEIS) located within the area of the project, in the south zone of the city. Two questions guided the investigation: (i) To what extent did the communities participate in the discussions regarding the proposal? and (ii) What were the governance arrangements that resulted from the proposal, considering the overlap of instruments within the UIP. It is highlighted that the institutional arrangements and the instruments selected for formulating and implementing the UIP increased the complexity of the project, the fragmentation of the process and the superficiality of the procedures for participating and legitimizing the intervention. Choices made during the UIP design and its “matryoshka” of instruments made it difficult for people to identify when and where crucial decisions were made regarding the intervention that affected low-income territories, thereby impairing social participation and postponing (even preventing) any resistance.
... It offers a framework to analyse the continuous need of actors to adapt to actions by others and to the overall emergent changes in the system. Evolution has been conceptualized as metatheory of change (Schneider, 2012), of emergent design without a central designer. In contrast, co-evolution allows the exploration of the scope for agency ('design') and how it is contingent on actions by others ('being designed'). ...
... Although there is a growing interest for these arrangements in the corporate governance literature (see e.g. Stiles, 2013), committees have been so far rather ignored by scholars. As noted by Adams et al. (2021): "Board committees have been relatively understudied" (p.1143). ...
In 2011, Britain and France introduced affirmative action policies aiming at improving board gender mix in listed companies. While the reforms were similar in terms of target and timing, Britain opted for a ‘soft law’ (comply or explain) approach, while France enacted a mandatory quota. Using difference-in-differences analyses, we examine the differential impact of these two reforms on board composition and on women empowerment within boards. We first show that the quota has been associated with a more rapid adjustment of the gender mix without significant disruptive effects on board composition. However, we report that the quota has induced a more limited access of women to monitoring committees within boards, relative to soft law. As these committees are the most influential, this evidence shows that the quota came at a cost when considering within-board women’s influence.
... As AI is implemented in various areas of everyday life, citizens are becoming increasingly aware of its positive and negative consequences. Poorly functioning AI systems in some countries, such as Australia and the 11 For overviews, see Bullock et al. (2022); Büthe et al. (2022). 12 Tallberg et al. (2023). ...
As the development and use of artificial intelligence (AI) continues to grow, policymakers are increasingly grappling with the question of how to regulate this technology. The most far-reaching international initiative is the European Union (EU) AI Act, which aims to establish the first comprehensive, binding framework for regulating AI. In this article, we offer the first systematic analysis of non-state actor preferences toward international regulation of AI, focusing on the case of the EU AI Act. Theoretically, we develop an argument about the regulatory preferences of business actors and other non-state actors under varying conditions of AI sector competitiveness. Empirically, we test these expectations using data from public consultations on European AI regulation. Our findings are threefold. First, all types of non-state actors express concerns about AI and support regulation in some form. Second, there are nonetheless significant differences across actor types, with business actors being less concerned about the downsides of AI and more in favor of lax regulation than other non-state actors. Third, these differences are more pronounced in countries with stronger commercial AI sectors. Our findings shed new light on non-state actor preferences toward AI regulation and point to challenges for policymakers balancing competing interests in society.
... From a governance perspective, human stakeholders made the final decision, not AI. This is an observation that we want to stress in light of the recent debates in governance studies and urban studies, in which scholars have been questioning the extent to which AI can actually make decisions (Bullock et al., 2022;Cugurullo, 2021b). In the case of the 2022 Shanghai outbreak detailed in Table 19.1, key decisions related to lockdowns and mass-testing strategies, for instance, were made by the Leading Group, i.e., human city managers, experts and policymakers. ...
In China, artificial intelligence (AI) features prominently in the national development strategy. However, there is a significant gap between the technological success of China’s AI revolution and citizens’ well-being, especially in relation to aspects of governance such as healthcare and education where AI applications have been relatively scarce. In order to address this issue, all levels of government are now following the direction set by the Chinese Communist Party in the attempt to integrate AI tech into people’ s daily life, and this is particularly evident in the healthcare sector. Since the COVID-19 pandemic, Chinese medical institutions and public health surveillance systems have utilized AI technology extensively in epidemiological investigations, medical diagnosis and treatment, and infection prevention, thereby narrowing the gap between China’s AI vision and its actual implementation in healthcare. The chapter analyses two urban software agents employed in China, specifically during the COVID-19 public health crisis. Subsequently, the chapter provides an overview of urban software agents designed to practice medicine (i.e., AI doctors) and discuss their use in urban China. Overall, the chapter offers a comparison between public and individual healthcare and sheds light on how AI is infiltrating these practices, particularly in cities.
... Una de las razones que han hecho que la gobernanza sea actualmente un concepto importante en las ciencias sociales es que conlleva imágenes y significados de cambio (Levy-Faur, 2012). Como lo advierte R. A. W. Rhodes (2012) al señalar que la gobernanza representa un cambio en la concepción de lo que es el gobierno, ya que desde este enfoque se entiende como nuevos procesos de gobierno, modificaciones en las reglas o nuevos métodos a través de los cuales se gobierna la sociedad. ...
... It offers a framework to analyse the continuous need of actors to adapt to actions by others and to the overall emergent changes in the system. Evolution has been conceptualized as metatheory of change (Schneider, 2012), of emergent design without a central designer. In contrast, co-evolution allows the exploration of the scope for agency ('design') and how it is contingent on actions by others ('being designed'). ...
A co-evolutionary theoretical framework offers new concepts and methods for communication governance researchers. These concepts and methods are particularly well suited to study problems with strong interdependencies between actors. Such problems often develop in a dynamic, open-ended way and are associated with high levels of uncertainty. Many important, pressing governance tasks in convergent communication sectors, such as efforts to regulate digital platforms, could benefit from integrating insights from co-evolutionary models. For some problems, such as global internet governance, co-evolutionary models may be the only way to develop a robust understanding of the available governance options. This chapter introduces the co-evolution concept, points to applications in communication governance research, and presents models and tools that could enrich future research. It also highlights the implications of its applications for communications governance, summarizes the strengths and limitations of the approach, and gives a brief outlook of further developments.
... Solutions envisioned by stakeholders are more likely to be regionally and contextually appropriate because stakeholders are aware of values and attitudes that would make solutions easier or more difficult to adopt, accepted by their communities (Buchecker et al., 2013) and more likely to be implemented (Luz, 2000) because stakeholders can advocate for implementation. Involvement of stakeholders reduces community perception that scientists are dictating solutions to communities without their input (Huxham et al., 2000;Ansell and Gash, 2008;Ansell, 2012;Emerson et al., 2012;Jones and White, 2022;Kliskey et al., In review). ...
Finding effective and practical solutions to climate change challenges in food-energy-water systems requires the integration of experts in local/regional social and biophysical systems, and these are commonly local community members. In the Magic Valley, Idaho we investigated the tensions between water used for energy and to irrigate cropland for food production, as well as, strategies for protecting water quantity and quality. Incorporating stakeholders with long-standing expertise allows the development of solutions to these challenges that are locally and regionally practical and consistent with the values of the social system into which they are incorporated. We describe a stakeholder-driven process used in a case study in the Magic Valley that incorporated local experts to develop plausible future scenarios, identify drivers of change, vet impact and hydrological modeling and map areas of change. The process described allowed stakeholders to envision alternative futures in their region, leading to development of enhanced context and place-based solutions and an anticipated time line for adoption of those solutions. The solutions developed by the stakeholders have been applied across many geographic areas. The described process can also be applied across a broad range of geographic levels. Most importantly, stakeholders should be involved in anticipating solutions and solution timing to the differing challenges posed by each scenario.
... Ansell & Gash, (2008) mendefinisikan collaborative governance sebagai pengaturan pemerintahan di mana terdapat satu atau lebih lembaga publik secara langsung melibatkan pemangku kepentingan dalam proses pengambilan keputusan kolektif yang formal, berorientasi konsensus, dan deliberatif dan yang bertujuan untuk membuat atau menerapkan kebijakan publik atau mengelola program atau aset publik. Ansell (2008) dalam buku The Oxford Handbook of Governance menjelaskan bahwa collaborative governance dapat dibedakan menjadi tiga, yaitu: collaborative planning, collaborative watershed, and regulatory negotiation. ...
The regulation about joint program in Ministry of Finance is only providing guidelines in The Head Office and Regional Office levels. The absence of guidelines for the smallest vertical unit has resulted in many Primary Tax Offices (PTO) and Customs and Excise Offices (CEO) Type C that have not carried out joint programs and loss a lot of potential revenue. This article is intended to examine the implementation of the joint program in the smallest vertical units. To examine, this articles uses a qualitative method with a case study approach. In-depth interviews, observations, and requests for various documents were conducted to collect data. The object of this research is Magelang PTO and Magelang CEO Type C. Based on data processing, the following conclusions are obtained. Magelang PTO and Magelang CEO Type C have not implemented a collaboration program. There are three challenges that have prevented the two offices from implementing the joint program, namely: the absence of guidelines for implementing joint programs in the smallest vertical units; there are cost and benefit considerations; there are different working areas. Beside that, based on the results from comparing data between excise payments and income tax payments, there is new potential revenue. Based on the research findings, policy makers in both agencies need to immediately formulate rules related to joint program for the smallest vertical units.
... In the spirit of this statement, Lahat and Zaba (2018) offer a model of three phases of tailored regulation that include the mapping phase of the population, its needs and the degree of risk to which it is subjected, the design phase of a mix of appropriate regulatory tools and service providers, and the implementation phase of clear role definition of the various actors, managing information collected and distributing it to relevant parties such as functionaries in the Ministry of Welfare, service providers and the field personnel involved (Rhodes, 2012). In the opinion of Lahat and Zaba (2018), strengthening the ability of practitioners and ongoing consideration of the needs of service recipients will lead to strengthening the regulatory capacity of the whole system and help provide better services. ...
The present article sets out to explore the relationship between the social worker’s identity and the organizational change within the Ministry of Welfare in Israel. The article builds on the observation that successful organizational change requires a good understanding of the impact of change on the identity of the employees. First, the study sets out to clarify the notion of regulation and indicates some current trends in the specialized literature regarding the regulation of social services. Next, an analysis is offered of the regulator’s role in Israel’s Ministry of Welfare, special attention being paid to a number of characteristics and challenges, such as: the regulator’s loneliness, regulatory capacity, and specialization and division (versus inclusion and expertise). A more detailed and nuanced analysis is then offered regarding the identity of the social worker, as a professional in the field of social welfare; specific attention is paid to the professional aspect, the legal-value aspect, and the organizational-institutional aspect. A concluding section brings together some of the key findings and implications of the present study.
... La première concerne la « gouvernance collaborative » (Ansell & Gash, 2008 ;Bingham, 2009 ;Ansell, 2012 ;Emerson, Nabatchi & Balogh, 2012), qui renvoie à l'étude des partenariats entre les organisations parties prenantes de la gouvernance. Sur le plan managérial, elle vise notamment à mettre les relations inter-organisationnelles au service de la création de connaissances (Ouadja, 2021). ...
Dans le contexte de réforme de la gouvernance du sport en France, l’analyse de l’articulation entre les politiques sportives fédérales et locales nécessite d’identifier les thématiques, niveaux d’échelles, déterminants et enjeux des relations entre les fédérations sportives et les collectivités territoriales. Il s’agit donc de « plonger au cœur » d’un phénomène social appréhendé comme l’une des composantes de la gouvernance multiscalaire du sport. Cette étude s’appuie, dans cette optique, sur une méthodologie principalement inductive (analyse documentaire, entretiens semi-directifs, observations immergées, etc.) ainsi que sur les apports théoriques de la sociologie de l’action publique.La première partie porte sur le « chantier » de la réforme de gouvernance du sport, engagé en France depuis 2018. Elle analyse le rôle joué par les groupes qui représentent les intérêts sportifs fédéraux et territoriaux dans cette séquence de la politique sportive nationale, envisagée comme un « terrain de jeu » fertile pour l’analyse. Une seconde partie s’intéresse aux ressources, représentations et intérêts de ces organisations en interrogeant les moteurs de leur convergence et/ou de leur confrontation. Ces résultats sont, enfin, affinés par une étude de cas portant sur la politique sportive de la communauté d’agglomération de Saint-Quentin-en-Yvelines. En les replaçant dans leur contexte historique, politique et organisationnel, il s’agit d’analyser les partenariats engagés par cette collectivité avec les fédérations françaises de cyclisme (autour de la programmation du Vélodrome National et de l’accueil des Jeux Olympiques et Paralympiques) et de golf (autour de l’organisation de la Ryder Cup).
This paper provides a philosophically informed and robust account of the global justice implications of Artificial Intelligence (AI). We first discuss some of the key theories of global justice, before justifying our focus on the Capabilities Approach as a useful framework for understanding the context-specific impacts of AI on low- to middle-income countries. We then highlight some of the harms and burdens facing low- to middle-income countries within the context of both AI use and the AI supply chain, by analyzing the extraction of materials, which includes mineral extraction and the environmental harms associated with it, and the extraction of labor, which includes unethical labor practices, low wages, and the trauma experienced by some AI workers. We then outline some of the potential harms and benefits that AI poses, how these are distributed, and what global justice implications this has for low- to middle-income countries. Finally, we articulate the global justice significance of AI by utilizing the Capabilities Approach. We argue that AI must be considered from a global justice perspective given that, globally, AI puts significant downward pressure on several elements of well-being thereby making it harder for people to achieve threshold levels of the central human capabilities needed for a life of dignity.
AI-related laws, standards, and norms are emerging rapidly. However, a lack of shared descriptive concepts and monitoring infrastructure undermine efforts to track, understand, and improve AI governance. We introduce AGORA (the AI Governance and Regulatory Archive), a rigorously compiled and enriched dataset of AI-focused laws and policies encompassing diverse jurisdictions, institutions, and contexts related to AI. AGORA is oriented around an original taxonomy describing risks, potential harms, governance strategies, incentives for compliance, and application domains addressed in AI regulatory documents. At launch, AGORA included data on over 330 instruments, with new entries being added continuously. We describe the manual and automated processes through which these data are systematically compiled, screened, annotated, and validated, enabling deep, efficient, and reliable analysis of the emerging AI governance landscape. The dataset, supporting information, and analyses are available through a public web interface (https://agora.eto.tech) and bulk dataset.
This essay examines how Artificial Intelligence (AI) systems are becoming more integral to international affairs by affecting how global governors exert power and pursue digital sovereignty. We first introduce a taxonomy of multifaceted AI payoffs for governments and corporations related to instrumental, structural, and discursive power in the domains of violence, markets, and rights. We next leverage different institutional and practice perspectives on sovereignty to assess how digital sovereignty is variously implicated in AI-empowered global governance. States both seek sovereign control over AI infrastructures in the institutional approach, while establishing sovereign competence through AI infrastructures in the practice approach. Overall, we present the digital sovereignty stakes of AI as related to entanglements of public and private power. Rather than foreseeing technology companies as replacing states, we argue that AI systems will embed in global governance to create dueling dynamics of public/private cooperation and contestation. We conclude with sketching future directions for IR research on AI and global governance.
Non-technical summary
Systemic risks such as climate change and pandemics are complex and interconnected. Managing such risks requires effective organisational structures and processes. This publication presents conceptually robust, evidence-based approaches for assessing and managing systemic risks.
Technical summary
Systemic risks originate and evolve in the nexus of tightly coupled dynamic systems, which are a characteristic of modern societies in the Anthropocene. Systemic risk implies the breakdown of a system which provides essential functions to society. Connectivity between systems is a key enabler for systemic risk to manifest through cascading effects. Thus, systemic risks originate and evolve in the nexus of tightly coupled dynamic systems. Cascading effects and the convergence of systemic risks with conventional risks as well as other systemic risks challenge the established modes of risk governance that still rest to a large extent on differentiation and compartmentalisation. Thus, governance of systemic risks requires an integrative approach towards risk governance that combines interdisciplinary risk analysis with iterative, adaptive and inclusive governance procedures. By drawing on the case studies of the COVID-19 pandemic and climate change, this paper proposes an innovative risk governance framework for systemic risks based on the integration of systems analysis and a governance procedure with the salient features of reflection, iteration, inclusion, transparency and accountability.
Social media summary
Systemic risks highlight the interconnected nature of our contemporary societies which calls for tailored responses.
Urban-Ecological Organisations (UEOs) exist to foster and develop knowledge and implementation of urban ecology by strengthening contacts, providing information, minimising environmental risks, and enriching the dialogue between researchers and practitioners. They also represent the interests of the government and address ecological issues posed by increasing urbanisation in the interest of the public. This becomes pertinent because the rising rates of urbanisation have created new challenges in the 21st Century. Therefore, there is a need for concerted efforts to squarely meet the challenges and maximise the opportunities afforded by urbanisation. It is critical that such efforts adopt a holistic view for achieving sustainable urban development, particularly within the context of climate change. Relying on secondary data, this review investigated the challenges and prospects of environmental governance and the role of UEOs as an instrument for managing Nigeria’s environment in a sustainable manner. The expected outcome will unravel governance challenges at the federal, state, and local levels in tackling environmental challenges issues in the country. The review shows that Nigeria’s environmental governance frameworks are ineffective in dealing with the magnitude of the country’s environmental challenges. Hence, the institutional and legislative frameworks for environmental governance must be comprehensive and effective. Also, it is crucial that the three tiers of government should show strong political will, while the UEOs in both public and private sectors should collaborate to drive the transformation towards sustainable urban development.
We are currently unsure whether the digital revolution will herald the end of work or whether it represents another evolutionary phase, similar to previous industrial revolutions. However, the changes in work brought about by AI and automation are already exerting negative impacts on both employment and people’s income. In this context, this article delves into the potential role of social law in mitigating these adverse effects. With this objective in mind, the author advocates for a departure from our current model of social inclusion in favour of broader income support mechanisms. The author expounds on how a universal basic income could contribute to steering the digital revolution with the aim of facilitating a more inclusive and effective transition into the digital era.
Urban regeneration and spatial planning have adopted a new participatory approach in recent decades, highlighting the importance of integrating the community in urban decision-making processes, especially in disadvantaged and socially excluded areas. In this context, the sociogram emerges as an essential tool for collaborative governance, allowing the visualization and analysis of the dynamics between the different actors involved. This study employs a comparative case study approach in three disadvantaged neighbourhoods in Córdoba, Spain, to examine how the sociogram can facilitate more effective and democratic participation in urban planning. Using heat maps, scatter plots and average analysis, relationships between actors are identified and characterized, providing a solid basis for more inclusive and equitable planning decisions. This analysis not only reveals the practical utility of the sociogram in participatory research but also underscores its theoretical relevance in building resilient and cohesive communities. Findings confirm the sociogram’s effectiveness in mapping stakeholder dynamics and enhancing participatory governance, ultimately fostering more informed and inclusive urban planning processes.
This paper examines what kind of innovation system the US and China have built through the ideological-institutional linkage and what kind of competitive dynamics this innovation system creates in the AI (Artificial Intelligence) field. China’s innovation system was characterized as a “loosely linked system” between the central and local governments. However, since Xi Jinping came to power, it has been transformed into a more centralized whole-nation system (举国体制) by adopting the innovation-driven development strategy (IDDS). In this transition process of a technology-security state, the Chinese central government’s AI development strategy, which emphasizes security purposes, is likely to conflict with the commercial purposes of local governments and sometimes produce results that are different from the central intention. Additionally, a securitized central AI strategy will inevitably reduce the space for coexistence with the American AI strategy. The US national security state has also undergone a significant process of disintegration since the end of the Cold War. However, it is facing a new turning point as China has risen as a new geopolitical competitor. Even in the United States, where the proportion and role of commercial actors are substantial, the federal government’s AI strategy is leading the direction of overall technological development. Despite differences in domestic systems, China and the United States, which have begun to recognize each other as major geopolitical enemies, are creating very similar strategic policies in the emerging field of AI. Ultimately, the performance in the artificial intelligence competition between the two countries will inevitably result in significant differences in the orientation and policy implementation process of the US and China for fostering the artificial intelligence technology field rather than the artificial intelligence policy itself. China is stepping up AI innovation by strengthening its whole-nation system, which could temporarily reduce the discrepancy between the central and local governments in the short term, but it could also serve as a basis for explaining the need for a stronger national security state for the US. The US is also seeking AI development by strengthening its national security state, but there is a possibility that it will not be able to move more agile than China.
This study explores whether AI is perceived as a threat or an opportunity and examines whether the perception of AI varies according to demographic factors, religious commitment, trust in science, beliefs in conspiracy theories, and previous experiences with AI utilizing data collected from 1443 participants. While most respondents believe that AI will simplify life (63%), increase efficiency (62%), and therefore, the development of AI should be encouraged (51%), a significant portion of the respondents is concerned that AI will increase unemployment (52%) and lead to social inequalities (47%). Around 21% of the respondents believe that AI may destroy humanity eventually. Respondents’ age, gender, occupation, religious commitment, beliefs in conspiracy theories, and previous experiences with AI (familiarity) significantly influence respondents’ perception of AI both as an opportunity and threat. Findings suggest that AI is paradoxically seen as a double-edged sword, perceived both as an opportunity and a threat, which indicates a pronounced confusion about AI among respondents.
Keywords: Artificial intelligencesocial impactsopportunitiesthreats
https://www.tandfonline.com/doi/full/10.1080/10447318.2023.2297114
One of the most profound expressions brought on by the so-called shift from government to governance is the adoption of temporary governance mechanisms or project organisations that call for increased speed in their experimentation and search for new innovations. However, the increasing use of projects also comes with challenges that have yet to be fully understood. The aim of the chapter is to discuss the benefits and challenges of temporary governance mechanisms in a public-sector context. The chapter critically examines how contemporary political-administrative systems and the complex ecology of actors with varying beliefs, principles and resources can be understood through theories of governance. Drawing on examples from EU-funded projects the chapter challenges common interpretations of collaborative governance and the ability of projects to achieve innovative results. It sheds light on project evaluation mechanisms and their ability to only capture project achievements and added value of project-based policy implementation to a limited extent. More emphasis should therefore be put on developing contextually sensitive interlinking mechanisms that contribute to the achievement of long-term outcomes from temporary outputs in political-administrative contexts. Consequently, these trends emphasise the paradox of stepping on the throttle while at the same time hitting the brakes.
Objetiva-se analisar formas de atuação dos Organismos Multilaterais (OM) no contexto governança educacional global. A base metodológica se sustenta pela abordagem qualitativa a partir da pesquisa bibliográfica e documental. Os dados apresentados permitem identificar um modus operandi de OM, como UNESCO, UNICEF e Banco Mundial, aperfeiçoado de forma a corroborar com a interdependência produzida na Governança Global, estabelecida pelos acordos da política internacional, envolvendo redes internacionais e domésticas. Nesse cenário, os OM vêm atuando como difusores e mediadores no processo de transferência de políticas, o que permite integrar redes híbridas de governança. Incidentemente, a transferência ocorre mediante a recontextualização e adaptação de políticas, de forma não linear, para a legitimação e o encaminhamento de agendas globais, sob a ostentação da ideia ilusória de que houve trocas, partilhas e negociações, mas que, em sua maior parte, corroborou para a transnacionalização da educação.
In the wake of the COVID-19 pandemic and the Russian invasion of Ukraine, many countries see coal as the easiest solution to their energy sector challenges, despite the consequences for climate goals. Several countries of the European Union started to re-evaluate their coal policies vis-à-vis the current energy crisis and, although such a change is expected to be short-term in nature, it nevertheless has negative consequences for the Union’s 2050 climate goal. However, most of the EU countries did not revise their phase-out goals. This paper examines Slovakia as a country that embarked on a coal phase-out trajectory only a few years before the pandemic broke out and stayed firmly on this path despite benefits stemming from the continued use of domestic coal. Domestic coal used to be considered a safeguard of energy security in Slovakia, especially after the 2009 gas crisis. However, a decision was made in 2018 to phase out coal by 2023, and this has not changed despite increased focus on domestic energy sources as energy security guarantors during the current energy crisis. This paper explains the decision in favour of a coal phase-out and its support vis-à-vis the energy crisis using the concept of ‘financial Europeanisation’, which stresses the importance of EU funds for the development of the domestic policies of EU member states. While the expected funds serve as a catalyst for the coal phase-out needed to reach climate goals, short-term advantages of revising a coal phase-out were outweighed by long-term benefits provided by EU funds.
This comparative paper adds to the literature by exploring the connection between policy entrepreneurship and collaboration among street-level bureaucrats (SLBs) in two countries. We asked if SLBs, as policy entrepreneurs, promote collaborative efforts in their work. If so, in what ways? The study was based on qualitative research and in-depth semi-structured interviews with 20 SLBs in social services in Israel and Germany. Our findings suggest that as policy entrepreneurs, SLBs use diverse ways of working together, and a higher level of policy change demands a higher level of collaboration. We offer three generic types of SLB policy entrepreneurs: collaborative policy entrepreneurs, collaborative-coordinator policy entrepreneurs, and coordinator-cooperative entrepreneurs. We suggest administrative cultures and policy styles may shed light on the presence of types of SLB policy entrepreneurs.
Climate change in Ethiopia’s capital city of Addis Ababa is characterized by an increase in rainfall and subsequent flooding and severe temperature with more heat waves. The city government has now recognized climate change as a serious threat, including it being a reason for loss of life and livelihoods. Even though governance has become a key mechanism to address a reduction in greenhouse-gas emissions and vulnerability to climate change, the practice of climate-change governance has been undermined by different factors. Thus, this study examined factors affecting climate-change governance in the city. The research adopted a mixed research design and depends on primary and secondary data sources. The binary logistic regression model and descriptive statistics were both used to analyse the quantitative data, while the descriptive method was used for the qualitative data. The results reveal that a lack of coordination, political will and leadership are the major factors that hinder the practice of governance in the city, followed by inadequate finance, policy, strategy, and regulation. In addition, a shortage of knowledgeable experts, lack of access to information and technologies had their own contributions to the ineffectiveness of climate-change governance. Thus, the city administration should place emphasis on climate change, giving it comparable weight to other crosscutting issues, and enabling the functioning of the steering committee with a strong accountability system. In addition, the city administration should take aggressive measures, including revising or formulating new policy, strategy or regulation, and even creating an independent institution for climate-change issues. Furthermore, the Addis Ababa City environmental protection and green development commission should create an enabling environment to attract non-state actors, in general, and NGOs, in particular, and should assign one directorate to mobilise finance, following the approach taken by the federal environmental protection commission. The commission should implement a mechanism to efficiently utilize the budget by applying continuous monitoring and evaluation. The commission should also provide continuous training and capacity building for leaders and experts at sub-city and Woreda levels.
While the world is still in the grasp of COVID‐19, countries are contemplating how to get their economies back on their feet. With a unique opportunity to do so in a sustainable manner, there is an urgent need to revisit the governance of climate change. Opportunities are clearly there: the resurgence in top‐down policies in the pandemic might spill‐over to climate governance; green economic stimuli might cause an increase in market‐based approaches; or an increased focus on solidarity, inclusion and collective buy‐in may drive more inclusive network‐based governance. Using the classic trichotomy of hierarchy, market and network governance, we have analysed the findings of 60 interviews with expert representatives from government, industry and third sector parties in the UK and the Netherlands. Their consideration of the key policies and measures needed to help the transition forward point towards a clear desire for a more hierarchical approach. In addition, mixing the three approaches, especially market and hierarchy, is considered the best way forward.
The current technology epoch—sometimes called the fourth industrial revolution (4IR)—involves the innovative application of rapidly advancing digital technologies such as artificial intelligence. Societal implications of the 4IR are significant and wide‐ranging, from life‐saving drug development to privacy loss and app addiction. A review of the information systems literature, however, reveals a narrow focus on technology‐enabled business benefits. Scant research attention has been paid to the role of humans and humanistic outcomes. To spur new research addressing these issues, formalised affordance theory is employed to develop a new 4IR conceptualization. Four groupings of affordances that capture salient 4IR action possibilities are developed within two categories: machine emulation of human cognition (expansive decision‐making and creativity automation) and machine emulation of human communication (relationship with humans and intermachine teaming). Implications are explored in the context of human‐machine coworking and the development of artificial intelligence safety regulations. Overall, the affordance conceptualization of the 4IR advances a new sociotechnical lexicon of action possibilities and their joint enactment in achieving humanistic and instrumental outcomes, enabling alignment of the scope of 4IR research with the scope of 4IR phenomena—and bringing humans back into the loop.
Transnational environmental law (TEL) is a relatively new field of research that is agitated by the concept of the Anthropocene. Like environmental law, TEL suffers from certain methodological challenges which are exacerbated by the sheer ammount of activity involved in the generation of transnational legal norms and regulation. This article argues that, despite some of these challenges, TEL is well placed to evolve further to better regulate human activity in the Anthropocene. It argues that to better reflect on what the Anthropocene means for TEL and how it may develop in practice, it must engage further with power, the problem of erasure of weaker actors, and the legacies of colonialism on the one hand, and the sciences – particularly Earth system science and ecology ‐ on the other. By doing so it would offer a more robust analysis of TEL and insights into its further development in practice.
One significant obstacle in establishing the sustainable business ecosystems described in the previous chapter is the regulatory environment. Too often, the law and other forms of regulation distort incentives for good actors and fail to respond effectively against the bad ones. This is hardly surprising. Much of the regulatory environment is designed for an old world of centralized and hierarchical corporations and the mitigation of information asymmetries and agency risk.
Large companies currently face an existential crisis because of a series of new pressures that have emerged over recent decades in a global, digital economy. The model of the centralized, hierarchical firm that was hugely successful in an earlier phase of industrial capitalism seems less well-equipped to meet the challenges of today, particularly the need to innovate continually. Moreover, the discourse and practice of corporate governance and other regulatory pressures have often pushed firms in the wrong direction, resulting in an unhealthy focus on managing agency risks, short-term financial gains, and formalistic compliance.
Most governments upscaled technological adaptations and integration into public service delivery during the COVID-19 pandemic policy responses globally. This article analyzes the context of open innovation (OI) applications and initiatives that characterized public innovation trends and impacted government agencies' responses to contain different consequences of the COVID-19 pandemic in four critical areas: economic recovery strategies, logistics and supply chain, digital health-care partnerships, and collaborations. We show how policy responses increased the uptake and upscaling of OI strategies in Kenya and South Africa. In both countries, government agencies, among other things, upgraded innovative or created online integrated portals for instant data sharing and used knowledge management platforms to monitor the COVID-19 pandemic prevalence in transportation systems and the delivery of vaccines. These enabled effective policy communication and tracing of COVID-19 patients, organizing the population for the vaccination drive, and generating timely data for further action in the four mentioned sectors.
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Lachapelle, Erick, Thomas Bergeron, Richard Nadeau, Jean-François Daoust, Ruth Dassonneville, and Éric Bélanger. 2021. "Citizens' Willingness to Support New Taxes for COVID-19 Measures and the Role of Trust." Politics & Policy 49(3): 534-65. https://doi.org/10.1111/polp.12404.Liu, Zezhao, and Zhengwei Zhu. 2021. "China's Pathway to Domestic Emergency Management: Unpacking the Characteristics in System Evolution." Politics & Policy 49(3): 619-50. https://doi.org/10.1111/polp.12407.Pelizzo, Riccardo, and Abel Kinyondo. 2014. "Public Accounts Committees in Eastern and Southern Africa: A Comparative Analysis." Politics & Policy 42(1): 77-102. https://doi.org/10.1111/polp.12062.
In this 60th anniversary of Business & Society essay, we seek to make three main contributions at the intersection of governance and artificial intelligence (AI). First, we aim to illuminate some of the deeper social, legal, organizational, and democratic challenges of rising AI adoption and resulting algorithmic power by reviewing AI research through a governance lens. Second, we propose an AI governance framework that aims to better assess AI challenges as well as how different governance modalities (architecture, laws, norms, and market) can support AI. At the heart of our framework lies the governance forces that apply to institutions, organizations, and individuals, who ultimately provide, regulate, and use AI decision-making. We discuss how businesses may harness AI’s economic power through governance solutions without creating or amplifying societal biases and inequalities. Third, as part of our section on future research, we identify a set of governance trade-offs in AI adoption, suggest future research avenues to conceptually strengthen research on the governance of AI, and lay out key policy recommendations.
Urban governance innovation is being framed as an imperative to address complex urban and global challenges, triggering the adoption of novel institutional forms, approaches and techniques. Urban political geographers are still some way off fully apprehending the dynamics of these innovations and their potential to reconfigure the composition and politics of urban governance. This paper suggests dialogue between urban political geography and public sector innovation literatures as a productive way forward. We build from this engagement to suggest a critical research agenda to drive systematic analysis of innovatory urban governance, its heterogeneous formation, politics and possibilities.
Can artificial intelligence (AI) be a sustainable way to help solving the Covid-19 global problem? What does the way how welfare states, charity organizations and labour markets are dealing with the pandemic crisis tells us about the AI capacity for reducing exposition of underprivileged groups to the desease? It is becoming more and more visible how the new coronavirus pandemic is affecting specifically the most deprived and vulnerable groups, and also the big difference that welfare states and their policies make. What did the pandemic show about the relations between social inequality, welfare state provision and AI? This presentation will discuss the role of AI as a tool for public policies fighting inequalities that were amplified during the Covid-19 crisis. It will be analysed how the welfare state, the labour market and social communities are already incorporating AI tools and how this can eventually produce more resilient paths. Accelareted and amplified by the Covid-19, several processes of AI penetration in health, education, healthcare, social security, public administrations, labour and surveillance of citizens, became a subject of public discussion. Artificial intelligence is currently a process of long-term change in health and biotechnologies, long-distance education, teleworking, automation, robotization, consumption behaviours, surveillance and human enhancement. An in-deep analysis of the Portuguese case will support the lessons that can be learnt from AI and its use in public policies in a context of pandemic crisis, leading to a set of political recommendations, to promote its application as a resilient tool to fight inequalities.
Commercial and social entrepreneurs are likely to help communities combat public health crises. Research on responses to pandemics has underappreciated the critical role of entrepreneurs. In the context of post-disaster response and recovery, entrepreneurs provide needed goods and services, repair and rebuild disrupted social networks, and can act as focal points for disaster survivors as they develop their plans to rebuild. During a pandemic, entrepreneurs perform similarly important economic and social functions. This article highlights these functions, including (1) providing the goods needed to survive and combat the pandemic, (2) performing the services needed so that people can stay productive and connected during the pandemic, and (3) acting as a source of community support and leadership. It also discusses how entrepreneurs are able to perform these roles despite operating in an environment that constricts the range and nature of entrepreneurial activity. Finally, this article describes a legal regime that will promote entrepreneurship during a pandemic.
COVID-19 as a global pandemic has had an unprecedented impact on the entire world. Projecting the future spread of the virus in relation to its characteristics for a specific suite of countries against a temporal trend can provide public health guidance to governments and organizations. Therefore, this paper presented an epidemiological comparison of the traditional SEIR model with an extended and modified version of the same model by splitting the infected compartment into asymptomatic mild and symptomatic severe. We then exposed our derived layered model into two distinct case studies with variations in mitigation strategies and non-pharmaceutical interventions (NPIs) as a matter of benchmarking and comparison. We focused on exploring the United Arab Emirates (a small yet urban centre (where clear sequential stages NPIs were implemented). Further, we concentrated on extending the models by utilizing the effective reproductive number (Rt) estimated against time, a more realistic than the static R0, to assess the potential impact of NPIs within each case study. Compared to the traditional SEIR model, the results supported the modified model as being more sensitive in terms of peaks of simulated cases and flattening determinations.
The COVID-19 pandemic has wreaked havoc in every country in the world, with serious health-related, economic, and social consequences. Since its outbreak in March 2020, many researchers from different fields have joined forces to provide a wide range of solutions, and the support for this work from artificial intelligence (AI) and other emerging concepts linked to intelligent data analysis has been decisive. The enormous amount of research and the high number of publications during this period makes it difficult to obtain an overall view of the different applications of AI to the management of COVID-19 and an understanding of how research in this field has been evolving. Therefore, in this paper, we carry out a scientometric analysis of this area supported by text mining, including a review of 18,955 publications related to AI and COVID-19 from the Scopus database from March 2020 to June 2021 inclusive. For this purpose, we used VOSviewer software, which was developed by researchers at Leiden University in the Netherlands. This allowed us to examine the exponential growth in research on this issue and its distribution by country, and to highlight the clear hegemony of the United States (USA) and China in this respect. We used an automatic process to extract topics of research interest and observed that the most important current lines of research focused on patient-based solutions. We also identified the most relevant journals in terms of the COVID-19 pandemic, demonstrated the growing value of open-access publication, and highlighted the most influential authors by means of an analysis of citations and co-citations. This study provides an overview of the current status of research on the application of AI to the pandemic.
The COVID-19 pandemic has worked as a catalyst, pushing governments, private companies, and healthcare facilities to design, develop, and adopt innovative solutions to control it, as is often the case when people are driven by necessity. After 18 months since the first case, it is time to think about the pros and cons of such technologies, including artificial intelligence—which is probably the most complex and misunderstood by non-specialists—in order to get the most out of them, and to suggest future improvements and proper adoption. The aim of this narrative review was to select the relevant papers that directly address the adoption of artificial intelligence and new technologies in the management of pandemics and communicable diseases such as SARS-CoV-2: environmental measures; acquisition and sharing of knowledge in the general population and among clinicians; development and management of drugs and vaccines; remote psychological support of patients; remote monitoring, diagnosis, and follow-up; and maximization and rationalization of human and material resources in the hospital environment.
Los algoritmos y la Inteligencia Artificial (IA) en el sector público se están adoptando en diferentes contextos. Conceptualmente, todavía no están claras las implicaciones de la IA y los algoritmos en las administraciones públicas. Sin embargo, diferentes organizaciones internacionales han definido una serie de principios éticos y cuestiones legales a tener en cuenta. Las instituciones de la Unión Europea se han convertido en el actor clave para guiar la adopción de IA, estableciendo las primeras líneas para las políticas públicas a nivel nacional. El Gobierno de España ha comenzado a desarrollar una estrategia en esa línea, si bien con unos responsables de políticas tecnológicas encargados de su implementación, que cuentan con sus propias percepciones y prioridades. Este artículo se inicia con las siguientes preguntas de carácter descriptivo y exploratorio: ¿Cómo están enmarcando ética y jurídicamente la IA los principales organismos internacionales? ¿Cuál es el marco de prioridades de la política de IA promovida por el Gobierno de España en relación con la Unión Europea? ¿Cuál es la percepción sobre la IA de los responsables de gestión de tecnologías (CIOs) en los ministerios? Para responder a estas preguntas, este trabajo se nutre de una revisión documental y de parte de los resultados de un cuestionario sobre las percepciones de los responsables de gestión de las tecnologías en los ministerios españoles. Nuestro análisis permite responder exploratoriamente las preguntas de investigación del estudio. Además, los resultados señalan algunas claves para diseñar políticas y estrategias de uso de la IA en diferentes contextos político-administrativos.
To lay the foundation for the special issue that this research article introduces, we present 1) a systematic review of existing literature on the implications of the use of Artificial Intelligence (AI) in public governance and 2) develop a research agenda. First, an assessment based on 26 articles on this topic reveals much exploratory, conceptual, qualitative, and practice-driven research in studies reflecting the increasing complexities of using AI in government – and the resulting implications, opportunities, and risks thereof for public governance. Second, based on both the literature review and the analysis of articles included in this special issue, we propose a research agenda comprising eight process-related recommendations and seven content-related recommendations. Process-wise, future research on the implications of the use of AI for public governance should move towards more public sector-focused, empirical, multidisciplinary, and explanatory research while focusing more on specific forms of AI rather than AI in general. Content-wise, our research agenda calls for the development of solid, multidisciplinary, theoretical foundations for the use of AI for public governance, as well as investigations of effective implementation, engagement, and communication plans for government strategies on AI use in the public sector. Finally, the research agenda calls for research into managing the risks of AI use in the public sector, governance modes possible for AI use in the public sector, performance and impact measurement of AI use in government, and impact evaluation of scaling-up AI usage in the public sector.
The year 2020 will always be in the history of mankind due to the deadly outbreak of COVID-19. Many people are already infected around the world due to the spreading of this novel coronavirus. The virus mainly replicates through close contacts, so there are no other alternatives than to keep social distance, use proper safety gear, and maintain self-quarantine. As a result, the growth of the virus has changed the lifestyle of every individual to a great extent. It is also compelling the Governments to dictate strict lock-downs of the highly affected areas, impose work-from-home approaches where applicable, enforce strict social distancing standards, and so on. Some of the countries are also using smartphone-based applications for contact tracing to track the possibly infected individuals. However, there is a lot of discussion around the world about these contact tracing applications and also about their architecture, attribute, data privacy, and so on. In this paper, we have provided a comprehensive review of these contact tracing approaches in terms of their system architecture, key attributes, and data privacy. We have also outlined a list of potential research directions that can improvise the tracing performance while maintaining the privacy of the user to a great extent.
This article examines how modes of governance are reconfigured as a result of using algorithms in the governance process. We argue that deploying algorithmic systems creates a shift toward a special form of design‐based governance, with power exercised ex ante via choice architectures defined through protocols, requiring lower levels of commitment from governing actors. We use governance of three policy problems – speeding, disinformation, and social sharing – to illustrate what happens when algorithms are deployed to enable coordination in modes of hierarchical governance, self‐governance, and co‐governance. Our analysis shows that algorithms increase efficiency while decreasing the space for governing actors' discretion. Furthermore, we compare the effects of algorithms in each of these cases and explore sources of convergence and divergence between the governance modes. We suggest design‐based governance modes that rely on algorithmic systems might be re‐conceptualized as algorithmic governance to account for the prevalence of algorithms and the significance of their effects.
We model and forecast the early evolution of the COVID-19 pandemic in Brazil using Brazilian recent data from February 25, 2020 to March 30, 2020. This early period accounts for unawareness of the epidemiological characteristics of the disease in a new territory, sub-notification of the real numbers of infected people and the timely introduction of social distancing policies to flatten the spread of the disease. We use two variations of the SIR model and we include a parameter that comprises the effects of social distancing measures. Short and long term forecasts show that the social distancing policy imposed by the government is able to flatten the pattern of infection of the COVID-19. However, our results also show that if this policy does not last enough time, it is only able to shift the peak of infection into the future keeping the value of the peak in almost the same value. Furthermore, our long term simulations forecast the optimal date to end the policy. Finally, we show that the proportion of asymptomatic individuals affects the amplitude of the peak of symptomatic infected, suggesting that it is important to test the population.
At the end of 2019, a new form of Coronavirus, called COVID-19, has widely spread in the world. To quickly screen patients with the aim to detect this new form of pulmonary disease, in this paper we propose a method aimed to automatically detect the COVID-19 disease by analysing medical images. We exploit supervised machine learning techniques building a model considering a data-set freely available for research purposes of 85 chest X-rays. The experiment shows the effectiveness of the proposed method in the discrimination between the COVID-19 disease and other pulmonary diseases.
The articles in this special section focus on government applications that use artificial intelligence (AI). The repercussions of artificial intelligence (AI) in government are broad and significant. The characteristics of these technologies will have an impact on almost everything in public organizations, from governance or the multidimensional perspective of interoperability, to the organizational or social implications linked to concepts like public value, transparency, or accountability. This special issue seeks to shed light on foundations and key elements to be taken into account for AI adoption by public organizations. Governments are the primary enablers of technology and market stimulators and regulators of general activities in our society. Governments have always sought the common good and, therefore, the advancement of public and collective interests. This is key to understanding, as a first step, why the principles of public-sector organizations do not always match those of the private sector. Public and private perspectives are very different, whether they be management, strategy, or policy.
As people around the world anxiously watched the early development of the novel coronavirus disease 2019 (COVID-19) pandemic, they expected Taiwan be one of the hardest-hit countries. Yet, to the surprise of many onlookers, the country has managed to keep severe acute respiratory syndrome coronavirus 2 at bay. Taiwan has taken decisive actions to prevent spread of the virus since the very beginning of the epidemic. While the fight is still ongoing, we provide an overview of major policies and strategies undertaken in Taiwan to tackle COVID-19 pandemic, analyzing them from a sociopolitical perspective. We found that the centralized and professional leadership, democratic and accountable political culture, and vibrant civil society and broad social participation are the key features of disease control in Taiwan.
Countries around the world have had to respond to the COVID-19 outbreak with limited information and confronting many uncertainties. Their ability to be agile and adaptive has been stressed, particularly in regard to the timing of policy measures, the level of decision centralization, the autonomy of decisions and the balance between change and stability. In this contribution we use our observations of responses to COVID-19 to reflect on agility and adaptive governance and provide tools to evaluate it after the dust has settled. Whereas agility relates mainly to the speed of response within given structures, adaptivity implies system-level changes throughout government. Existing institutional structures and tools can enable adaptivity and agility, which can be complimentary approaches. However, agility sometimes conflicts with adaptability. Our analysis points to the paradoxical nature of adaptive governance. Indeed, successful adaptive governance calls for both decision speed and sound analysis, for both centralized and decentralized decision-making, for both innovation and bureaucracy, and both science and politics.
The coronavirus (COVID-19) pandemic has overwhelmed many national healthcare systems around the world. In attempts to meet their emergency needs and mitigate escalating challenges, governments are increasingly reaching out to the private sector to form sustainable, public-private partnerships (PPPs). Unfortunately, many of these ad hoc efforts have been reactive and uncoordinated to date. This perspective article thus offers a proactive, collaborative, and strategic vision for healthcare PPPs, focusing on short-, medium-, and long-term proposals that will harmonize strategic objectives and mobilize both public and private resources to combat and build resilience against global pandemics like COVID-19.
COVID-19 as a Complex Intergovernmental Problem - Mireille Paquet, Robert Schertzer
Predictive computing tools are increasingly being used and have demonstrated successfulness in providing insights that can lead to better health policy and management. However, as these technologies are still in their infancy stages, slow progress is being made in their adoption for serious consideration at national and international policy levels. However, a recent case evidences that the precision of Artificial Intelligence (AI) driven algorithms are gaining in accuracy. AI modelling driven by companies such as BlueDot and Metabiota anticipated the Coronavirus (COVID-19) in China before it caught the world by surprise in late 2019 by both scouting its impact and its spread. From a survey of past viral outbreaks over the last 20 years, this paper explores how early viral detection will reduce in time as computing technology is enhanced and as more data communication and libraries are ensured between varying data information systems. For this enhanced data sharing activity to take place, it is noted that efficient data protocols have to be enforced to ensure that data is shared across networks and systems while ensuring privacy and preventing oversight, especially in the case of medical data. This will render enhanced AI predictive tools which will influence future urban health policy internationally.
Objective
To review and critically appraise published and preprint reports of prediction models for diagnosing coronavirus disease 2019 (covid-19) in patients with suspected infection, for prognosis of patients with covid-19, and for detecting people in the general population at risk of being admitted to hospital for covid-19 pneumonia.
Design
Rapid systematic review and critical appraisal.
Data sources
PubMed and Embase through Ovid, Arxiv, medRxiv, and bioRxiv up to 24 March 2020.
Study selection
Studies that developed or validated a multivariable covid-19 related prediction model.
Data extraction
At least two authors independently extracted data using the CHARMS (critical appraisal and data extraction for systematic reviews of prediction modelling studies) checklist; risk of bias was assessed using PROBAST (prediction model risk of bias assessment tool).
Results
2696 titles were screened, and 27 studies describing 31 prediction models were included. Three models were identified for predicting hospital admission from pneumonia and other events (as proxy outcomes for covid-19 pneumonia) in the general population; 18 diagnostic models for detecting covid-19 infection (13 were machine learning based on computed tomography scans); and 10 prognostic models for predicting mortality risk, progression to severe disease, or length of hospital stay. Only one study used patient data from outside of China. The most reported predictors of presence of covid-19 in patients with suspected disease included age, body temperature, and signs and symptoms. The most reported predictors of severe prognosis in patients with covid-19 included age, sex, features derived from computed tomography scans, C reactive protein, lactic dehydrogenase, and lymphocyte count. C index estimates ranged from 0.73 to 0.81 in prediction models for the general population (reported for all three models), from 0.81 to more than 0.99 in diagnostic models (reported for 13 of the 18 models), and from 0.85 to 0.98 in prognostic models (reported for six of the 10 models). All studies were rated at high risk of bias, mostly because of non-representative selection of control patients, exclusion of patients who had not experienced the event of interest by the end of the study, and high risk of model overfitting. Reporting quality varied substantially between studies. Most reports did not include a description of the study population or intended use of the models, and calibration of predictions was rarely assessed.
Conclusion
Prediction models for covid-19 are quickly entering the academic literature to support medical decision making at a time when they are urgently needed. This review indicates that proposed models are poorly reported, at high risk of bias, and their reported performance is probably optimistic. Immediate sharing of well documented individual participant data from covid-19 studies is needed for collaborative efforts to develop more rigorous prediction models and validate existing ones. The predictors identified in included studies could be considered as candidate predictors for new models. Methodological guidance should be followed because unreliable predictions could cause more harm than benefit in guiding clinical decisions. Finally, studies should adhere to the TRIPOD (transparent reporting of a multivariable prediction model for individual prognosis or diagnosis) reporting guideline.
Systematic review registration
Protocol https://osf.io/ehc47/ , registration https://osf.io/wy245 .
Public administration research has documented a shift in the locus of discretion away from street-level bureaucrats to “systems-level bureaucracies” as a result of new information communication technologies that automate bureaucratic processes, and thus shape access to resources and decisions around enforcement and punishment. Advances in artificial intelligence (AI) are accelerating these trends, potentially altering discretion in public management in exciting and in challenging ways. We introduce the concept of “artificial discretion” as a theoretical framework to help public managers consider the impact of AI as they face decisions about whether and how to implement it. We operationalize discretion as the execution of tasks that require nontrivial decisions. Using Salamon’s tools of governance framework, we compare artificial discretion to human discretion as task specificity and environmental complexity vary. We evaluate artificial discretion with the criteria of effectiveness, efficiency, equity, manageability, and political feasibility. Our analysis suggests three principal ways that artificial discretion can improve administrative discretion at the task level: (1) increasing scalability, (2) decreasing cost, and (3) improving quality. At the same time, artificial discretion raises serious concerns with respect to equity, manageability, and political feasibility.
It is the aim of this chapter to summarize the theoretical lessons to be drawn from the wealth of literature produced by more than thirty years of implementation research. The chapter is structured as follows: Section 2 discusses three different analytical approaches in traditional implementation theory in more detail: top-down models, bottom-up critiques, and hybrid theories that try to combine elements of the two other strands of literature. We explicate the theoretical underpinnings and discuss the pros and cons of the respective approaches. Section 3 then provides an overview of more recent theoretical approaches to implementation, all of which depart from central underpinnings of traditional implementation studies. In particular, we address insights gained from the study of implementation processes in the context of the European Union and we discuss the interpretative approach to implementation, which follows an alternative ontological path. Section 4 focuses on the main insights gained from more than thirty years of implementation research for a proper understanding of implementation processes. Moreover, it discusses the contributions of implementation analysis to the wider field of policy analysis and political science. Finally, Section 5 identifies a number of persistent weaknesses of implementation analysis and concludes by suggesting possible directions of future research to overcome these weaknesses in the years to come.
This paper is the introduction to the special issue entitled: ‘Governing artificial intelligence: ethical, legal and technical opportunities and challenges'. Artificial intelligence (AI) increasingly permeates every aspect of our society, from the critical, like urban infrastructure, law enforcement, banking, healthcare and humanitarian aid, to the mundane like dating. AI, including embodied AI in robotics and techniques like machine learning, can improve economic, social welfare and the exercise of human rights. Owing to the proliferation of AI in high-risk areas, the pressure is mounting to design and govern AI to be accountable, fair and transparent. How can this be achieved and through which frameworks? This is one of the central questions addressed in this special issue, in which eight authors present in-depth analyses of the ethical, legal-regulatory and technical challenges posed by developing governance regimes for AI systems. It also gives a brief overview of recent developments in AI governance, how much of the agenda for defining AI regulation, ethical frameworks and technical approaches is set, as well as providing some concrete suggestions to further the debate on AI governance.
This article is part of the theme issue ‘Governing artificial intelligence: ethical, legal, and technical opportunities and challenges’.
The nascent adoption of Artificial Intelligence (AI) in the public sector is being assessed in contradictory ways. But while there is increasing speculation about both its dangers and its benefits, there is very little empirical research to substantiate them. This study aims at mapping the challenges in the adoption of AI in the public sector as perceived by key stakeholders. Drawing on the theoretical lens of framing, we analyse a case of adoption of the AI system IBM Watson in public healthcare in China, to map how three groups of stakeholders (government policy-makers, hospital managers/doctors, and IT firm managers) perceive the challenges of AI adoption in the public sector. Findings show that different stakeholders have diverse, and sometimes contradictory, framings of the challenges. We contribute to research by providing an empirical basis to claims of AI challenges in the public sector, and to practice by providing four sets of guidelines for the governance of AI adoption in the public sector.
The data science technologies of artificial intelligence (AI), Internet of Things (IoT), big data and behavioral/predictive analytics, and blockchain are poised to revolutionize government and create a new generation of GovTech start-ups. The impact from the ‘smartification’ of public services and the national infrastructure will be much more significant in comparison to any other sector given government’s function and importance to every institution and individual. Potential GovTech systems include Chatbots and intelligent assistants for public engagement, Robo-advisors to support civil servants, real-time management of the national infrastructure using IoT and blockchain, automated compliance/regulation, public records securely stored in blockchain distributed ledgers, online judicial and dispute resolution systems, and laws/statutes encoded as blockchain smart contracts. Government is potentially the major ‘client’ and also ‘public champion’ for these new data technologies. This review paper uses our simple taxonomy of government services to provide an overview of data science automation being deployed by governments world-wide. The goal of this review paper is to encourage the Computer Science community to engage with government to develop these new systems to transform public services and support the work of civil servants.
Artificial intelligence has become an important tool for governments around the world. However, it is not clear to what extent artificial intelligence can improve decision-making, and some policy domains have not been the focus of most recent studies, including the public budget process. More specifically, budget allocation is one of the areas in which AI may have greatest potential. Therefore, this study attempts to contribute to this gap in our existing knowledge by answering the following research question: To what extent can artificial intelligence techniques help distribute public spending to increase GDP, decrease inflation and reduce the Gini index? In order to respond to this question, this article proposes an algorithmic approach on how budget inputs (specific expenditures) are processed to generate certain outputs (economic, political, and social outcomes). The authors use the multilayer perceptron and a multiobjective genetic algorithm to analyze World Bank Open Data from 1960 to 2019, including 217 countries. The advantages of implementing this type of decision support system in public expenditures allocation arise from the ability to process large amounts of data and to find patterns that are not easy to detect, which include multiple non-linear relationships. Some technical aspects of the expenditure allocation process could be improved with the help of these kinds of techniques. In addition, the results of the AI-based approach are consistent with the findings of the scientific literature on public budgets, using traditional statistical techniques.
Artificial intelligence (AI) offers challenges and benefits to the public sector. We present an ethical framework to analyze the effects of AI in public organizations, guide empirical and theoretical research in public administration, and inform practitioner deliberation and decision making on AI adoption. We put forward six propositions on how the use of AI by public organizations may facilitate or prevent unnecessary harm. The framework builds on the theory of administrative evil and contributes to it in two ways. First, we interpret the theory of administrative evil through the lens of agency theory. We examine how the mechanisms stipulated by the former relate to the underlying mechanisms of the latter. Specifically, we highlight how mechanisms of administrative evil can be analyzed as information problems in the form of adverse selection and moral hazard. Second, we describe possible causal pathways of the theory of administrative evil and associate each with a level of analysis: individual (micro), organizational (meso), and cultural (macro). We then develop both descriptive and normative propositions on AI’s potential to increase or decrease the risk of administrative evil. The article hence contributes an institutional and public administration lens to the growing literature on AI safety and value alignment.
During a pandemic, tourism can inflict negative social costs on communities in tourist destinations. This study examines factors affecting residents’ responses to policies to mitigate the social costs of tourism during a pandemic. Two hypothetical scenarios are analyzed. Study 1 investigates framing effects on residents’ attitudes toward the effectiveness of policy measures; study 2 explores the impact of mental accounting on residents’ willingness to pay. Findings show that residents perceive policy measures as more effective if their positive outcomes of such measures are highlighted. Also, residents are more willing to fund social cost mitigation with unearned income, such as anti-pandemic bonds, than through their salaries. This article contributes to academic debate on the efficacy of public policies in combating pandemics and extends the literature on framing and mental accounting in tourism research. Policy implications of these findings are also discussed.
Despite the current popularity of artificial intelligence (AI) and a steady increase in publications over time, few studies have investigated AI in public contexts. As a result, assumptions about the drivers, challenges, and impacts of AI in government are far from conclusive. By using a case study that involves a large research university in England and two different county councils in a multiyear collaborative project around AI, we study the challenges that interorganizational collaborations face in adopting AI tools and implementing organizational routines to address them. Our findings reveal the most important challenges facing such collaborations: a resistance to sharing data due to privacy and security concerns, insufficient understanding of the required and available data, a lack of alignment between project interests and expectations around data sharing, and a lack of engagement across organizational hierarchy. Organizational routines capable of overcoming such challenges include working on-site, presenting the benefits of data sharing, reframing problems, designating joint appointments and boundary spanners, and connecting participants in the collaboration at all levels around project design and purpose.
As the COVID-19 pandemic causes unprecedented disruptions in citizens' lives and work, prompting a wide range of responses from governments across the globe. The southern Indian state of Kerala, India's COVID-19 “ground zero”, stands out with a fatality rate at a fraction of other richer Indian states and countries. This has happened despite the state presenting strong vulnerabilities to COVID-19. Using the theoretical lens of frugal innovation, I analyse how the Kerala State Government (KSG) combated the spread of COVID-19. This research uncovers the mechanisms at play as KSG implemented and used frugal technologies as platforms that helped decision making and strategy to fight the pandemic. I find a rich interplay of frugal innovations promoted by the government, in partnership with research institutes and private sector actors, which are cheap and efficacious. The study defines and promotes the concept of government frugal innovation (GFI) and provides valuable insights and tools to help governments navigate and effectively respond to this crisis, encouraging the rest of the world to learn from Kerala's experience. My conceptual model characterizes GFI as involving collaborative aspects, and holds practical implications beyond the times of crises.
The governance of public sector organizations has been challenged by the growing adoption and use of Artificial Intelligence (AI) systems and algorithms. Algorithmic transparency, conceptualized here using the dimensions of accessibility and explainability, fosters the appraisal of algorithms’ footprint in decisions of public agencies, and should include impacts on civil servants’ work. However, although discretion will not disappear, AI innovations might have a negative impact on how public employees support their decisions. This article is intended to answer the following research questions: RQ1. To what extent algorithms affect discretionary power of civil servants to make decisions?RQ2. How algorithmic transparency can impact discretionary power of civil servants? To do so, we analyze SALER, a case based on a set of algorithms focused on the prevention of irregularities in the Valencian regional administration (GVA), Spain, using a qualitative methodology supported on semi-structured interviews and documentary analysis. Among the results of the study, our empirical work suggests the existence of a series of factors that might be linked to the positive impacts of algorithms on the work and discretionary power of civil servants. Also, we identify different pathways for achieving algorithmic transparency, such as the involvement of civil servants in active development, or auditing processes being recognized by law, among others.
How can political elites learn from the past to enhance sustainability of their leadership in a pandemic situation? In this article, we develop a theoretical framework of policy implementation that combines collaboration from public and private sectors ("Public-Private Partnership," or PPP) to efficiently deal with urgent crises such as COVID-19. We explain the role of new institutions prompted by policy failure precedence (Time 1) that at a later time period (Time 2) allow for the activation of PPPs with the aim to extend the political life of incumbent leaderships. Specifically, we examine the case of South Korea, a country in which a prior case of MERS in 2015 (Time 1) had established new policies for pandemic governance. In 2020, such policies were activated by the incumbent leadership in order to contain COVID-19 (Time 2). In particular, for swift and effective management of the pandemic, the South Korean government utilized partnerships with the private sector to exponentially increase the amount of Real-Time Polymerase Chain Reaction (RT-PCR) testing. We apply Policy Feedback Theory to demonstrate the political effects of failed policy precedents and how the political outcomes again shape new policies in a dynamic and cyclical manner. Empirically, we conduct a content analysis of South Korea's pharmaceutical sector in government procurement and exports of test-kits during the COVID-19 pandemic. We show that as the pandemic situation progressed, South Korea's leader, who had been in danger of plummeting support to the extent that impeachment was discussed as a viable option, drastically shifted public opinion to achieve a landslide victory in general elections in April 2020. Our findings suggest that democratic governments, aware of precedents and wary of their fate in elections, are pressured to perform well in crisis management, and thus turn to rapidly mobilizing public and private means for survival. Such means are evidenced by the case of emergency use authorization (EUA) process for test-kits, in which "leapfrogging players" - up-and-coming innovators - that contribute to turning a pandemic crisis into an opportunity for sustainable leadership and for themselves.
Public budgeting is at the core of any government, since decisions about how to use resources affect all areas of public policy and government programs. With emerging technologies like artificial intelligence, new opportunities may exist to improve the public budgeting process. Although recent research has focused on many topics related to public budgeting, there is still a gap in terms of our knowledge about the potential role of artificial intelligence techniques. While the potential advantages of using intelligent algorithms for optimization in the private sector have been studied, there are also potential benefits that are unique to the public sector, particularly in terms of improved decision-making. This study proposes a methodology based on artificial intelligence to explore the optimization of the Mexican federal government’s public budget distribution. The main outcomes explored are related to social development, economic development, government, and non-programmed budget items. The findings indicate that investment in social development in Mexico should be increased and the non-program-based budget should be reduced. We acknowledge that many other factors influence the allocation of public budgets to different policy domains and specific government programs, including political and environmental variables, but think it is useful to have a proposed “optimal” solution to better understand the differences between policy priorities and budget allocations and the causes of those differences.
The rise of Big, Open and Linked Data (BOLD) enables Big Data Algorithmic Systems (BDAS) which are often based on machine learning, neural networks and other forms of Artificial Intelligence (AI). As such systems are increasingly requested to make decisions that are consequential to individuals, communities and society at large, their failures cannot be tolerated, and they are subject to stringent regulatory and ethical requirements. However, they all rely on data which is not only big, open and linked but varied, dynamic and streamed at high speeds in real-time. Managing such data is challenging. To overcome such challenges and utilize opportunities for BDAS, organizations are increasingly developing advanced data governance capabilities. This paper reviews challenges and approaches to data governance for such systems, and proposes a framework for data governance for trustworthy BDAS. The framework promotes the stewardship of data, processes and algorithms, the controlled opening of data and algorithms to enable external scrutiny, trusted information sharing within and between organizations, risk-based governance, system-level controls, and data control through shared ownership and self-sovereign identities. The framework is based on 13 design principles and is proposed incrementally, for a single organization and multiple networked organizations.
Purpose
This paper aims to show how robotic technology is being used to combat the COVID-19 pandemic.
Design/methodology/approach
Following a short introduction, this discusses the role of robots in the following COVID-related applications: disinfection, checking human temperature, monitoring public places, delivering food and other items, food preparation and personal interactions by telepresence. It concludes with a brief discussion.
Findings
Robots are playing diverse and vital roles. They have helped to reduce the chances of spreading the infection by reducing inter-personal contact; freed-up medical professionals by conducting certain routine teaks; assisted and speeded-up the provision of food and medical supplies; monitored public places; informed the public of the need for social distancing; and allowed those in isolation to remain in contact with friends and family.
Originality/value
This provides a timely account of the use of robots in efforts to ameliorate the impact of the COVID-19 pandemic.
This article explores the imprecise boundary between Lethal Autonomous Weapons Systems (LAWS) and Human-Machine Teaming – as a subset of Human-Machine Interaction – and the extent both are emerging as a point of concern (and option) in military and security policy debates. As the development of Human-Machine Teaming relates to artificial intelligence (AI) capabilities there also exists an area of concern pertaining to reliability and confidence, particularly in the heat of battle. Also known as Manned-Unmanned Teaming, Human-Machine Teaming attempts to engender trust and collaborative partnerships with robots and algorithms. Clearly the prospect of LAWS in recent times, or so-called ‘killer robots,’ has raised questions relating to the degree such devices can be trusted to select and engage targets without further human intervention. Aside from examining the ‘trust factor,’ the article also considers security threats posed by both state and non-state actors and the complicit yet inadvertent role multinational corporations play in such developments where civilian technology is modified for dual-purposes. The effectiveness of government regulation over AI, including whether AI can be ‘nationalised’ for national security reasons, will also be examined as part of AI non-proliferation.
Understanding the early transmission dynamics of diseases and estimating the effectiveness of control policies play inevitable roles in the prevention of epidemic diseases. To this end, this paper is concerned with the design of optimal control strategies for the novel coronavirus disease (COVID-19). A mathematical model of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) transmission based on Wuhan’s data is considered. To solve the problem effectively and efficiently, a multi-objective genetic algorithm is proposed to achieve high-quality schedules for various factors including contact rate and transition rate of symptomatic infected individuals to the quarantined infected class. By changing these factors, two optimal policies are successfully designed. This study has two main scientific contributions that are: (1) This is pioneer research that proposes policies regarding COVID-19, (2) This is also the first research that addresses COVID-19 and considers its economic consequences through a multi-objective evolutionary algorithm. Numerical simulations conspicuously demonstrate that by applying the proposed optimal policies, governments could find useful and practical ways for control of the disease.
As government and public administration lag behind the rapid development of AI in their efforts to provide adequate governance, they need respective concepts to keep pace with this dynamic progress. The literature provides few answers to the question of how government and public administration should respond to the great challenges associated with AI and use regulation to prevent harm. This study analyzes AI challenges and former AI regulation approaches. Based on this analysis and regulation theory, an integrated AI governance framework is developed that compiles key aspects of AI governance and provides a guide for the regulatory process of AI and its application. The article concludes with theoretical implications and recommendations for public officers.
Advances in artificial intelligence (AI) have attracted great attention from researchers and practitioners and have opened up a broad range of beneficial opportunities for AI usage in the public sector. Against this background, there is an emerging need for a holistic understanding of the range and impact of AI-based applications and associated challenges. However, previous research considers AI applications and challenges only in isolation and fragmentarily. Given the lack of a comprehensive overview of AI-based applications and challenges for the public sector, our conceptual approach analyzes and compiles relevant insights from scientific literature to provide an integrative overview of AI applications and related challenges. Our results suggest 10 AI application areas, describing their value creation and functioning as well as specific public use cases. In addition, we identify four major dimensions of AI challenges. We finally discuss our findings, deriving implications for theory and practice and providing suggestions for future research.
Big Data and artificial intelligence will have a profound transformational impact on governments around the world. Thus, it is important for scholars to provide a useful analysis on the topic to public managers and policymakers. This study offers an in-depth review of the Policy and Administration literature on the role of Big Data and advanced analytics in the public sector. It provides an overview of the key themes in the research field, namely the application and benefits of Big Data throughout the policy process, and challenges to its adoption and the resulting implications for the public sector. It is argued that research on the subject is still nascent and more should be done to ensure that the theory adds real value to practitioners. A critical assessment of the strengths and limitations of the existing literature is developed, and a future research agenda to address these gaps and enrich our understanding of the topic is proposed.
In The Semisovereign People, E. E. Schattschneider asserts, “the defi nition of the alternatives is the supreme instrument of power” (Schattschneider 1960/1975, 66). The definition of alternative issues, problems, and solutions is crucial because it establishes which issues, problems, and solutions will gain the attention of the public and decision makers and which, in turn, are most likely to gain broader attention. This chapter considers the processes by which groups work to elevate issues on the agenda, or the process by which they seek to deny other groups the opportunity to place issues. Of particular importance is the fact that is not merely issues that reach the agenda, but the construction or interpretation of issues competes for attention. The discussion is organized into four major parts. In the fi rst, I review the agenda-setting process and our conceptions of how agendas are set. In the second part, I consider the relationships between groups, power, and agenda setting. In the third part, I discuss the relationship between the construction of problems and agenda setting. I conclude this chapter with a discussion of contemporary ways of measuring and conceiving of the agenda as a whole and the composition of the agenda.
I regret that copyright restrictions make it impossible for me to share this chapter in this archive. I would ask that you request an interlibrary loan copy through your library.
There is an implicit assumption in most policy studies that once a policy has been formulated the policy will be implemented. This assumption is invalid for policies formulated in many Third World nations and for types of policies in Western societies. Third World governments tend to formulate broad, sweeping policies, and governmental bureaucracies often lack the capacity for implementation. Interest groups, opposition parties, and affected individuals and groups often attempt to influence the implementation of policy rather than the formulation of policy.
A model of the policy implementation process is presented. Policy implementation is seen as a tension generating force in society. Tensions are generated between and within four components of the implementing process: idealized policy, implementing organization, target group, and environmental factors. The tensions result in transaction patterns which may or may not match the expectations of outcome of the policy formulators. The transaction patterns may become crystallized into institutions. Both the transaction patterns and the institutions may generate tensions which, by feedback to the policymakers and implementors, may support or reject further implementation of the policy.
By application of the model, policymakers can attempt to minimize disruptive tensions which can result in the failure of policy outcomes to match policy expectations.