No full-text available
To read the full-text of this research,
you can request a copy directly from the author.
Beyond the bubble that is Robodebt: How governments that lose integrity threaten democracy
Abstract
Robodebt describes the automated process of matching the Australian Taxation Office's income data with social welfare recipients' reports of income to Centrelink. Discrepancies signalling benefit overpayment trigger debt notices. The scheme has been criticised for inaccurate assessment, illegality, shifting the onus of proof of debt onto welfare recipients, poor support and communication, and coercive debt collection. Beyond immediate concerns of citizen harm, Robodebt harms democratic governance. Through persisting with Robodebt, the government is launching a regulatory assault on its own integrity. Two Senate inquiries reveal government endorsing (1) incoherence and inconsistency in public engagement, (2) unsound purposes and processes and (3) disregard for citizens. Such actions destroy trustworthiness. Citizens keep their distance and as a result, cooperation falters. At particular risk is the tax system. Citizens harmed by government turn to alternative authorities for help and opportunity, not always along legitimate pathways. The underground economy provides one such opportunity for fearful welfare recipients.
... While misconceptions about personal data in the commercial sector might lead to lost customers and reduced revenue due to wrong inference, within government initiatives they can lead to severe and costly mismanagement of programs, for example in the public health or social security domains, as well as the more general loss in the overall trust in governments by the public [14,49,54]. In the context of research, assumptions and misconceptions about population data and how these are used for research studies, potentially after being linked with other data, can lead to wrong outcomes that can result in conclusions with severe negative impact in the real world [12,34]. ...
... In more recent times such techniques have seen much wider use in domains ranging from business intelligence to social science research. Within governments, record linkage is, for example, being used to find welfare fraudsters [14,21], and in national security to identify terrorism suspects [18]. Modern record linkage techniques are based on sophisticated statistical or machine learning based approaches [25,45] and are capable of producing high quality linked data sets. ...
... Unless relevant metadata describing such changes are available, it can be challenging to identify any changed definitions because the change might only have subtle effects on the characteristics of the population of interest for a research study. (14) Temporal data aspects do not matter. Given the dynamic nature of personal details, the time and date when population data are measured and included into a database can be crucial because differences in data lag can lead to inconsistent data making them not suitable for research studies [13,34]. ...
Databases covering all individuals of a population are increasingly used for research studies in domains ranging from public health to the social sciences. There is also growing interest by governments and businesses to use population data to support data-driven decision making. The massive size of such databases is often mistaken as a guarantee for valid inferences on the population of interest. However, population data have characteristics that make them challenging to use, including various assumptions being made how such data were collected and what types of processing have been applied to them. Furthermore, the full potential of population data can often only be unlocked when such data are linked to other databases, a process that adds fresh challenges. This article discusses a diverse range of misconceptions about population data that we believe anybody who works with such data needs to be aware of. Many of these misconceptions are not well documented in scientific publications but only discussed anecdotally among researchers and practitioners. We conclude with a set of recommendations for inference when using population data.
... Other forms of policy can constrain the ways that people can use their financial capability, based upon specific personal factors. The recent 'Robodebt' policy that matched financial records from the Australian Tax Office to Centrelink data resulted in some people having false debts created, and then having to deal with debt collection agencies, in some cases leading to a loss of money for people, many of whom were in low-income households [227]. Many individuals who were not familiar with debt collection processes were unsure of how to handle their debt notice, which resulted in debts that were falsely raised being paid [227]. ...
... The recent 'Robodebt' policy that matched financial records from the Australian Tax Office to Centrelink data resulted in some people having false debts created, and then having to deal with debt collection agencies, in some cases leading to a loss of money for people, many of whom were in low-income households [227]. Many individuals who were not familiar with debt collection processes were unsure of how to handle their debt notice, which resulted in debts that were falsely raised being paid [227]. ...
This report brings together current research to provide a theoretical model of the current financial wellbeing environment in the Australian context. We describe how financial wellbeing is related to people, how the pandemic has depleted financial wellbeing for many, how it relates to policy and how various programs have responded to financial wellbeing issues. The complex interactions between the different dimensions that drive financial wellbeing demonstrate the need for a more nuanced approach to financial wellbeing that can integrate these different areas into an overall model of financial wellbeing. We argue that there needs to be more attention given to structural drivers of financial wellbeing, and that adopting a systems approach to financial wellbeing is the best way to do this. While there are a number of actors in the Australian ecosystem who work to drive structural change, and who already are employing systems-based approaches, there is scope for greater coordination in these efforts.
... While suitable help was arranged to assist the individual, it is a reminder of the profound responsibility researchers have during the design phase of diverse digital solutions suitable for addressing mental health problems, and specifcally, for the end-users of our innovations. Others have identifed that the potential for harm/s to arise when using AI is a factor for consideration when developing technological innovations for people who have preexisting or emerging mental illness and/or suicidality [2]. Additionally, the release of the World Health Organization's (WHO) Ethics and Governance of Artifcial Intelligence in Health Care suggests that future innovations should be iatrogenically sound to ensure the safety of at-risk populations [3]. ...
Background. The prominence of technology in modern life cannot be understated. However, for some people, these innovations or their related plausible advancements can be associated with perceptual misinterpretation and/or incorporation into delusional concepts. Objective. This paper aims to explore the intersection of technological advancement and experiencing psychosis. We present a discussion about the explanation seeking that incorporates the concept, that for some people, of technological innovation becoming intertwined with delusional symptoms over the past 100 years. Methods. A longitudinal review of the literature was conducted to synthesize and draw these concepts together, mapping them to a timeline that aligns computing science and healthcare expertise and presents the significant technological changes of the modern era charted against mental health milestones and reports of technology-related delusions. Results. It is possible for technology to be incorporated into the content of delusions with evidence supporting a link between the rate of technological change, the content of delusions, and the use of technology as a way of seeking an explanation. Moreover, analysis suggests a need to better understand how innovations may impact the mental health of people at risk of psychosis and other mental health conditions. Conclusions. Clinical experts and lived experience experts need to be informed about and collaborate with future research and development of technology, specifically artificial intelligence and machine learning, early in the development cycle. This concurs with other artificial intelligence research recommendations calling for design attention to the development and implementation of technological innovation applied in a mental health context.
... Such over-expectations might cause costly mismanagement in areas such as public health or in government decision-making. Furthermore, failing population data projects, such as census operations or health surveillance, might even result in the loss of trust in governments and science by the public [8,11]. In the context of research, myths and misconceptions 1 about population data can lead to wrong outcomes of research studies that can result in conclusions with severe negative impact [12,13]. ...
Databases covering all individuals of a population are increasingly used for research and decision-making. The massive size of such databases is often mistaken as a guarantee for valid inferences. However, population data have characteristics that make them challenging to use. Various assumptions on population coverage and data quality are commonly made, including how such data were captured and what types of processing have been applied to them. Furthermore, the full potential of population data can often only be unlocked when such data are linked to other databases. Record linkage often implies subtle technical problems, which are easily missed. We discuss a diverse range of myths and misconceptions relevant for anybody capturing, processing, linking, or analysing population data. Remarkably, many of these myths and misconceptions are due to the social nature of data collections and are therefore missed by purely technical accounts of data processing. Many are also not well documented in scientific publications. We conclude with a set of recommendations for using population data.
... Skandalen kunne skje fordi forvaltningen forhastet implementeringen og ikke hadde utviklet gode nok etiske krav, i tillegg til at det var fravaer av egnede demokratiske eller rettslige kontrollordninger, samt manglende åpenhet og vektlegging av rettsstats-og lovgivningskrav (Carney, 2018). Det er pekt på at skandalen har et slikt omfang at den er egnet til å undergrave tilliten til den australske staten (Braithwaithe, 2020). ...
Artificial intelligence is increasingly used to streamline the Norwegian welfare state. The use of artificial intelligence challenges the current mechanisms to ensure that the welfare state is kept within the rule of law. The capacity and digitization of the welfare services, and, in particular, the use of artificial intelligence, entails a considerable risk of systemic error. There are already manifold examples of things that can go spectacularly wrong. The Norwegian rule of law principles must be rethought, and there is a need to articulate a new rule of law principle – systematic work to prevent systematic errors. Only with such principles integrated in digitalization processes can artificial intelligence serve to strengthen the welfare state.
*Original text in Norwegian:
Det er stor iver etter å effektivisere den norske velferdsforvaltningen ved hjelp av kunstig intelligens. Bruken av kunstig intelligens utfordrer imidlertid de systemene vi i dag har for å sikre at rettsstatlige prinsipper etterleves i velferdsforvaltningen. Digitaliseringen av masseforvaltningen, og særlig bruk av kunstig intelligens, innebærer en høy risiko for masseproduksjon av feil. Det er allerede mange eksempler på at det kan gå virkelig galt. Den norske rettsstatstenkningen må videreutvikles, og det er behov for et nytt prinsipp: systematisk arbeid for å forebygge systematiske feil. Kun hvis rettsstatsprinsipper integreres i digitaliseringsarbeidet, kan kunstig intelligens styrke velferdsstaten.
... Braithwaite 2020. ...
Thomas R. Dye’s much cited definition of public policy as whatever governments choose to do or not do, i.e., government action and inaction, helps us understand the parameters of what policy is but says very little about the dynamics that produce government policy choice. Critical policy studies offers one way to understand these dynamics, the power relations that produce them, and a means to evaluate policy against democratic and social justice values. Critical policy studies is different to more rationalist forms of policy analysis, in that it rejects the notion that policy can be designed and implemented in a neutral and scientific fashion, free from interests, values and ideologies. This claim, and scholarly focus, is important to note as it underpins critical policy studies’ research themes – the analysis of the social construction of policies to unpack common knowledge, perceptions, values, ideologies and power relations, and evaluate them against social justice and democratic ideals and values.
The chapter proceeds in three main sections. Firstly, the origins of critical policy studies are examined and critical policy studies is defined. Critical policy studies’ relation, and reaction, to the work of Harold Laswell and the policy sciences is especially examined. Secondly, the relation of critical theory to critical policy studies is unpacked, sketching the links between Marxist theory to present day critical theory. In the third section, three common critical policy studies themes are analysed: technocratic policy, power and democracy; social construction in the policy process; and policy discourses. A case study in Australian politics and policy is provided for each theme: Robodebt; sexual and gender based violence; and COVID governance of Indigenous communities. The chapter concludes by drawing out key themes for students of critical policy studies to use in their own analyses and evaluations of policy.
... Organizations should indeed uphold their own moral fortitude amidst any possible problems with employees' personal values or any flaws in character that could motivate them to flout institutional rules. Therefore, there may be a disagreement between these different sorts of reliability and conflict between promoting personal integrity and developing incorruptible institutions and processes (Braithwaite, 2020;Seibel, W. 2020). Moreover, to ensure the employees act ethnically and with integrity in the private sector. ...
The study examines the mediation effect of employee accountability on the relationship between working conditions and organizational health. The data were collected using a survey questionnaire on a sample of 311 elementary school teachers from public schools in North and South District of Kiblawan, Davao del Sur. The study employed a correlational and causal approach using Path Analysis to determine the relationships between working conditions, organizational health, and employee accountability. Findings revealed that working conditions and organizational health are positively and significantly related. Moreover, there is also a significant and positive relationship between working conditions and employee accountability. Results also indicated that employee accountability and organizational health are significantly and positively related. Using the Path Analysis, the mediation model suggested that employee accountability partially mediates the positive relationship between working conditions and organizational health. Specifically, the total effect of working conditions on organizational health is mediated by or passes through employee accountability. The remaining is attributed to the direct impact of working conditions or indirect effect through the mediation of other variables that are not considered in the study.
... The resulting bureaucratic inhumanity is evident in millions of unanswered phonecalls every year (Dingwall 2018;Whyte 2020b) and the 'Robodebt' scandal-an automated debt-recovery program aimed at income-support beneficiaries that was ultimately ruled illegal by the Federal Court (Medhora 2019). Valerie Braithwaite (2020) argues the harms of Robodebt go beyond the immediate harm to citizens, to harming trust in government and threatening democracy. ...
... This finding shows that the replacement of people-centered services with robots and machines is a real fear for consumers. This may be attributed to people obtaining much of their understanding from popular media (ie, films [52]) or past negative experiences with common automated services such as banking (which was a comparison noted by many participants) or the very poorly received Australian debt recovery program, Robodebt [53]. Such preconceptions about automation clearly had a major impact on the reasons community and help-seeker participants provided for not using Lifeline's services if technology enhancements were introduced, which would need to be carefully addressed if AI is to be used effectively to support human decision-making processes in crisis support contexts. ...
Background
Emerging technologies, such as artificial intelligence (AI), have the potential to enhance service responsiveness and quality, improve reach to underserved groups, and help address the lack of workforce capacity in health and mental health care. However, little research has been conducted on the acceptability of AI, particularly in mental health and crisis support, and how this may inform the development of responsible and responsive innovation in the area.
Objective
This study aims to explore the level of support for the use of technology and automation, such as AI, in Lifeline’s crisis support services in Australia; the likelihood of service use if technology and automation were implemented; the impact of demographic characteristics on the level of support and likelihood of service use; and reasons for not using Lifeline’s crisis support services if technology and automation were implemented in the future.
Methods
A mixed methods study involving a computer-assisted telephone interview and a web-based survey was undertaken from 2019 to 2020 to explore expectations and anticipated outcomes of Lifeline’s crisis support services in a nationally representative community sample (n=1300) and a Lifeline help-seeker sample (n=553). Participants were aged between 18 and 93 years. Quantitative descriptive analysis, binary logistic regression models, and qualitative thematic analysis were conducted to address the research objectives.
Results
One-third of the community and help-seeker participants did not support the collection of information about service users through technology and automation (ie, via AI), and approximately half of the participants reported that they would be less likely to use the service if automation was introduced. Significant demographic differences were observed between the community and help-seeker samples. Of the demographics, only older age predicted being less likely to endorse technology and automation to tailor Lifeline’s crisis support service and use such services (odds ratio 1.48-1.66, 99% CI 1.03-2.38; P<.001 to P=.005). The most common reason for reluctance, reported by both samples, was that respondents wanted to speak to a real person, assuming that human counselors would be replaced by automated robots or machine services.
Conclusions
Although Lifeline plans to always have a real person providing crisis support, help-seekers automatically fear this will not be the case if new technology and automation such as AI are introduced. Consequently, incorporating innovative use of technology to improve help-seeker outcomes in such services will require careful messaging and assurance that the human connection will continue.
... How and for what purpose AI is implemented partly determines whether benefits or harms are generated from its use. For example, an algorithm autonomously tasked with determining welfare payments, without meaningful human oversight, and ultimately making inaccurate calculations is a deployment context that can generate harms (Braithwaite, 2020). Or AI tasked with assessing employee performance to input into, and potentially communicate, termination decisions raises questions regarding the transparency of data collection and the appropriateness of deploying the technology for such purposes (Obedkov, 2021). ...
Artificial intelligence (AI) is increasingly inputting into various human resource management (HRM) functions, such as sourcing job applicants and selecting staff, allocating work, and offering personalized career coaching. While the use of AI for such tasks can offer many benefits, evidence suggests that without careful and deliberate implementation its use also has the potential to generate significant harms. This raises several ethical concerns regarding the appropriateness of AI deployment to domains such as HRM, which directly deal with managing sometimes sensitive aspects of individuals’ employment lifecycles. However, research at the intersection of HRM and technology continues to largely center on examining what AI can be used for, rather than focusing on the salient factors relevant to its ethical use and examining how to effectively engage human workers in its use. Conversely, the ethical AI literature offers excellent guiding principles for AI implementation broadly, but there remains much scope to explore how these principles can be enacted in specific contexts-of-use. By drawing on ethical AI and task-technology fit literature, this paper constructs a decision-making framework to support the ethical deployment of AI for HRM and guide determinations of the optimal mix of human and machine involvement for different HRM tasks. Doing so supports the deployment of AI for the betterment of work and workers and generates both scholarly and practical outcomes.
Thomas R. Dye’s much cited definition of public policy as whatever governments choose to do or not do – that is, government action and inaction – helps us to understand the parameters of what policy is but says very little about the dynamics that produce government policy choice. The field of critical policy studies offers one way to understand these dynamics, the power relations that produce them and a means to evaluate policy against democratic and social justice values. Critical policy studies is different from more rationalist forms of policy analysis in that it rejects the notion that policy can be designed and implemented in a neutral and scientific fashion, free from interests, values and ideologies. This claim, and scholarly focus, is important to note as it underpins the research themes of critical policy studies – the analysis of the social construction of policies to unpack common knowledge, perceptions, values, ideologies and power relations, and evaluate them against social justice and democratic ideals and values.
The chapter proceeds in three main sections. Firstly, the origins of critical policy studies are examined and critical policy studies is defined. The relation, and reaction, of critical policy studies to the work of Harold Lasswell and the policy sciences is especially examined. Secondly, the relation of critical theory to critical policy studies is unpacked, sketching the links between Marxist theory to present-day critical theory. In the third section, three common critical policy studies themes are analysed: technocratic policy, power and democracy; social construction in the policy process; and policy discourses. The chapter concludes by drawing out key themes for students of critical policy studies to use in their own analyses and evaluations of policy.
Artificial intelligence (AI) and algorithmic decision making are having a profound impact on our daily lives. These systems are vastly used in different high-stakes applications like healthcare, business, government, education, and justice, moving us toward a more algorithmic society. However, despite so many advantages of these systems, they sometimes directly or indirectly cause harm to the users and society. Therefore, it has become essential to make these systems safe, reliable, and trustworthy. Several requirements, such as fairness, explainability, accountability, reliability, and acceptance, have been proposed in this direction to make these systems trustworthy. This survey analyzes all of these different requirements through the lens of the literature. It provides an overview of different approaches that can help mitigate AI risks and increase trust and acceptance of the systems by utilizing the users and society. It also discusses existing strategies for validating and verifying these systems and the current standardization efforts for trustworthy AI. Finally, we present a holistic view of the recent advancements in trustworthy AI to help the interested researchers grasp the crucial facets of the topic efficiently and offer possible future research directions.
With the advent of highly effective antiviral treatment for hepatitis C, many people have undergone treatment and been cured. Others, however, have not undergone treatment, even where it is free and readily available. Australia's aim of eliminating the disease by 2030 means this group is of concern to researchers, health professionals and policymakers.
This article draws on 50 interviews conducted for a research project on treatment experiences to examine treatment non-uptake in Australia. Informed by Berlant's (2007) work on ‘slow death’, it analyses experiences of non-uptake to explain the dynamics at work in such outcomes.
The analysis is divided into three parts. First, participant Cal describes a lifetime in which hepatitis C, homelessness and prison have shaped his outlook and opportunities. Second, Evan describes intergenerational drug consumption, family contact with the prison system and an equally long history with hepatitis C. Finally, Rose also describes a long history of hepatitis C, complex struggles to improve life and contact with the prison system. All three accounts illuminate the dynamics shaping treatment decisions, calling to mind Berlant's slow death as a process of being ‘worn out by the activity of reproducing life’ under conditions that both demand self-management, and work against it.
In concluding, the article points to Berlant's distinction between ‘epidemics’ and ‘endemics’, arguing that its politics apply directly to hepatitis C. In doing so, it highlights the need to address the criminalising, pathologising, capitalist context of ‘attrition’ (Berlant) that wears out lives even as it fetishises autonomy, responsibility and choice.
The Australian National Disability Insurance Scheme (NDIS) allocates funds to participants for purchase of services. Only one percent of the 89,299 participants spent all of their allocated funds with 85 participants having failed to spend any, meaning that most of the participants were left with unspent funds. The gap between the allocated budget and realised expenditure reflects misallocation of funds. Thus we employ alternative machine learning techniques to estimate budget and close the gap while maintaining the aggregate level of spending. Three experiments are conducted to test the machine learning models in estimating the budget, expenditure and the resulting gap; compare the learning rate between machines and humans; and identify the significant explanatory variables. Results show that machines learn “faster” than humans; machine learning models can improve the efficiency of the NDIS implementation; and significant explanatory variables identified by decision tree models and regression analysis are similar.
This paper explores the force of automation and its contradictions and resistances within (and beyond) the financial sector, with a specific focus on computational practices of credit-scoring and lending. It examines the operations and promotional discourses of fintech start-ups LendUp.com and Elevate.com that offer small loans to the sub-prime consumers in exchange for access to their online social media and mobile data, and Zest AI and LenddoEFL that sell automated decision-making tools to verify identity and assess risk. Reviewing their disciplinary reputational demands and impacts on users and communities, especially women and people of colour, the paper argues that the automated reimagination of credit and creditability disavows the formative design of its AI and redefines moral imperatives about character to align with the interests of digital capitalism. The economic, social and cultural crises precipitated by the Covid-19 pandemic have only underscored the internal contradictions of these developments, and a variety of debt resistance initiatives have emerged, aligned with broader movements for social, economic, and climate justice around the globe. Cooperative lending circles such as the Mission Asset Fund, activist groups like #NotMyDebt, and Debt Collective, a radical debt abolition movement, are examples of collective attempts to rehumanize credit and debt and resist the appropriative practices of contemporary digital finance capitalism in general. Running the gamut from accommodationist to entirely radical, these experiments in mutual aid, debt refusal, and community-building provide us with roadmaps for challenging capitalism and re-thinking credit, debt, power, and personhood within and beyond the current crises.
We undoubtably live in a digitally infused world. From government administrative processes to financial transactions and social media posts, digital technologies automatically collect, collate, combine and circulate digital traces of our actions and thoughts, which are in turn used to construct digital personas of us. More significantly, government decisions are increasingly automated with real world effect; companies subvert human workers to automated processes; while social media algorithms prioritise outrage and ‘fake news’ with destabilizing and devastating effects for public trust in social institutions. Accordingly, what it means to be a person, a citizen, and a consumer, and what constitutes society and the economy in the 21 st century is profoundly different to that in the 20 th century.
Taking a multi-disciplinary perspective (including public health, sociology, criminology, and political science amongst others) and using examples from across the globe, this book provides a detailed understanding of the complex and highly contested nature of drug policy, drug policy making, and the theoretical perspectives that inform the study of drug policy. It draws on four different theoretical perspectives: evidence-informed policy, policy process theories, democratic theory, and post-structural policy analysis.
The use of and trade in illegal drugs are a global phenomenon. It is viewed by governments as a significant social, legal, and health problem that shows no signs of abating. The key questions explored throughout this book are what governments and other bodies of social regulation should do about illicit drugs, including drug policies aimed at improving health and reducing harm, drug laws and regulation, and the role of research and values in policy development. Seeing policy formation as dynamic iterative interactions between actors, ideas, institutions, and networks of policy advocates, the book explores how policy problems are constructed and policy solutions selected, and how these processes intersect with research evidence and values. This then animates the call to democratise drug policy and bring about inclusive meaningful participation in policy development in order to provide the opportunity for better, more effective, and value-aligned drug policies. This book will be of great interest to students and scholars of drug policy from a number of disciplines, including public health, sociology, criminology, and political science.
In light of the growing need to pay attention to general public opinions and sentiments toward AI, this paper examines the levels of understandings amongst the Australian public toward the increased societal use of AI technologies. Drawing on a nationally representative survey of 2019 adults across Australia, the paper examines how aware people consider themselves to be of recent developments in AI; variations in popular conceptions of what AI is; and the extent to which levels of support for AI are liable to alter with additional exposure to information about AI. While a majority of respondents consider themselves to have little knowledge and familiarity with the topic of AI, the survey nevertheless finds considerable range of relatively ‘plausible’ basic understandings of what AI is. Significantly, repeated questioning highlights a willingness among many people to reassess their opinions once having received further information about AI, and being asked to think through issues relating to AI and society. These patterns remain relatively consistent, regardless of respondents’ political orientation, income, social class and other demographic characteristics. As such, the paper concludes by considering how these findings provide support for the development of public education efforts to further enhance what might be termed ‘public understanding of AI’.
This Special Issue addresses the use of linked data for research purposes and to carry out government functions such as child protection, allocation of resources, and debt recovery. Government investment in big data has the potential to change citizens' experience of the welfare state in a broad range of areas in both positive and negative ways. It is therefore important that the Australian social policy community understands and engages with the potential benefits and risks involved in the linkage and analysis of government datasets. Papers in this Special Issue discuss the technical challenges and institutional barriers involved in the construction and governance of linked government data assets and showcase the promise of big data for generating policy relevant insights. This Special Issue also features papers critically interrogating the potential for big data to produce social harms. We contextualise this collection of papers with a brief history of recent policy developments in regards to access to government held data. We also discuss ways of improving public trust and social licence for the use of big data and argue that the voices of First Nations and disadvantaged Australians must be given greater weight in discussions of how their data will be used.
Australia has a comprehensive system of social security but, as Solomon explains, it adopts a welfare or charity-based rather than a rights-based approach referencing its obligations at international law. The country’s institutional framework gives no constitutional protection, no bill of rights and no right to social security at common law with social security decisions made solely within the political and policy space. The country’s majoritarian political impulses serve to socially exclude welfare recipients and its embrace of neoliberalism has given this an economic rather than a social/poverty reduction policy focus with the individual rather than society taking the risk. A ‘workfare’ focus and some privatisation of the social security system have resulted in accessibility to social security support being highly constrained even if formally available.
The regulatory welfare state illuminates path dependencies and tendencies to mutual growth in markets, welfare, and regulation. This article uses two specific welfare-to-work programs, one in Korea and one in Australia, to illustrate the institutional interconnections that are in play within the regulatory welfare state. Governance of these programs is hampered by lack of discursive capacity to identify where problems exist and how they can be fixed. When faced with new programs, implementers look to higher authorities to make sense of and to solve the problems on the ground, but authorities are blinded by old institutional categories that pit market mentalities against welfare mentalities with regulation as an ideological tool, rather than an integral part of solutions. Transparency and cross-boundary listening are necessary to create the bridging capital to make these programs work and reconnect democratically elected governments with their citizens.
Surveillance and governance of the poor have been key foci for the critical social policy literature for some time, although scholarship regarding the role of big data in social policy – and how this might differ from previous forms of surveillance – is continuing to emerge. This article contributes to this literature by exploring the use of big data under Australia and Aotearoa/New Zealand's social investment approaches. Both seek to replace social policy spending with “investment” expected to produce future fiscal returns; in the words of a former Aotearoa/New Zealand Prime Minister, they aim to do “more with less”. We argue that big data often provides a poor evidence base for social investment and can, instead, be better understood as part of a panoptic toolkit for poverty governance, mediating continuous surveillance, examination and normalisation of the body politic by enabling the discursive (re)construction of people's identities and subjectivities. This construction serves to exacerbate social inequalities and undermine the rights of ordinary citizens.
As smart technologies such as artificial intelligence (AI), automation and Internet of Things (IoT) are increasingly embedded into commercial and government services, we are faced with new challenges in digital inclusion to ensure that existing inequalities are not reinforced and new gaps that are created can be addressed. Digital exclusion is often compounded by existing social disadvantage, and new systems run the risk of creating new barriers and harms. Adopting a case study approach, this paper examines the exclusionary practices embedded in the design and implementation of social welfare services in Australia. We examined Centrelink’s automated Online Compliance Intervention system (‘Robodebt’) and the National Disability Insurance Agency’s intelligent avatar interface ‘Nadia’. The two cases show how the introduction of automated systems can reinforce the punitive policies of an existing service regime at the design stage and how innovative AI systems that have the potential to enhance user participation and inclusion can be hindered at implementation so that digital benefits are left unrealised.
This article asks how rule of law institutions failed to ‘bell the cat’ on the illegality of Centrelink's robo-debt programme and its unethical character. It identifies serious structural deficiencies in the design of accountability and remedial avenues at seven different levels. It argues for adherence to Administrative Review Council guidelines on machine learning, Parliamentary accounting of Ombudsman and Audit agencies on rule of law foundations and model litigant protocols, attention to ethical administration, redacted publication of selected first tier Administrative Appeals Tribunal rulings, contractual guarantees of independence in legal aid/advocacy funding, building of pro bono advocacy partnerships, and cultural change designed to counter stigmatisation of the vulnerable.
This article reviews Australia’s social security online compliance initiative (‘OCI’) to determine whether the Senate Community Affairs References Committee was right to recommend that its administering agency (Centrelink) resume responsibility for obtaining all information necessary for calculating working age payment debts based on verifiable actual fortnightly earnings rather than on the basis of assumed averages, or whether responsibility has always remained with Centrelink when the person is unable to easily provide records. It argues that legal responsibility ultimately has always rested with Centrelink in such cases and outlines distributional justice and best practice reasons why the OCI system should be brought into compliance with the law
Australia s national system of social security reached its centenary in June 2008. This article provides a broad overview of how social security has developed in Australia over the last 100 years or so and reflects on how the system has come to be as it is now. Although much has changed in that time, there are strong elements of continuity as well particularly the prevalence of means tests, the use of funding from general revenue, and the strong emphasis on participation. It is noted that Australian model of social security differs markedly from the international norm. Nevertheless, it has proven to be remarkably resilient since its inception a century ago such that arrangements akin to social insurance (the usual model elsewhere) have, as a result, mainly developed in the private sector. Maximising economic and social participation has also been a cornerstone of Australia s system. The authors speculate that, given the relative stability demonstrated by the system so far, 100 years from now the essential elements of Australia s social security system may well remain intact.
Every day, we make decisions on topics ranging from personal investments to schools for our children to the meals we eat to the causes we champion. Unfortunately, we often choose poorly. The reason, the authors explain, is that, being human, we all are susceptible to various biases that can lead us to blunder. Our mistakes make us poorer and less healthy; we often make bad decisions involving education, personal finance, health care, mortgages and credit cards, the family, and even the planet itself. Thaler and Sunstein invite us to enter an alternative world, one that takes our humanness as a given. They show that by knowing how people think, we can design choice environments that make it easier for people to choose what is best for themselves, their families, and their society. Using colorful examples from the most important aspects of life, Thaler and Sunstein demonstrate how thoughtful "choice architecture" can be established to nudge us in beneficial directions without restricting freedom of choice. Nudge offers a unique new take-from neither the left nor the right-on many hot-button issues, for individuals and governments alike. This is one of the most engaging and provocative books to come along in many years. © 2008 by Richard H. Thaler and Cass R. Sunstein. All rights reserved.
Why an institution's rules and regulations are obeyed or disobeyed is an important question for regulatory agencies. This paper discusses the findings of an empirical study that shows that the use of threat and legal coercion as a regulatory tool--in addition to being more expensive to implement--can sometimes be ineffective in gaining compliance. Using survey data collected from 2,292 taxpayers accused of tax avoidance, it will be demonstrated that variables such as trust need to be considered when managing noncompliance. If regulators are seen to be acting fairly, people will trust the motives of that authority, and will defer to their decisions voluntarily. This paper therefore argues that to shape desired behavior, regulators will need to move beyond motivation linked purely to deterrence. Strategies directed at reducing levels of distrust between the two sides may prove particularly effective in gaining voluntary compliance with an organization's rules and regulations.
Neoliberal reforms and ring-wing ideologies have seen the ideal of the social security ‘safety net’ take a hammering in the UK, USA and Australia. While the gap between rich and poor has widened, and demand for welfare payments increased, politicians, certainly in Australia, have generally neglected low income families, preferring to twiddle the economic dials affecting middle and upper income earners instead. Of course, tussling over who pays tax, how much, what constitutes useful expenditure, and who receives welfare services and benefits is not new – these questions have attended the modern welfare state from its inception. But the welfare safety net that most of us, grudgingly or otherwise, concede to be necessary for collective social harmony is no longer proving as effective as we would wish. Even with a battered and frayed, but still ostensibly functional systems of welfare payment and support offered in Australia, the number of people experiencing perpetual disadvantage is rising, with intergenerational poverty – its increase and impacts on children – of particular concern.
Although it is part of core government business to collect information about its citizens, ‘big data’ has increased the scale, speed and complexity of data collection and use to such an extent that it is arguably qualitatively different from the record-keeping that has gone before it. Big data represents a radical shift in the balance of power between State and citizen. This article argues that embedding big data in government operations masks its deployment as enhancing government power, rather than simply facilitating execution of government activities. In other words, big data is ‘disruptive’ technology that calls for the examination of the limits of government power. To illustrate this argument, this article examines a selection of recent case studies of attempts by the Australian government to deploy big data as a tool of governance. It identifies the risk to the citizen inherent in the use of big data, to justify review of the bounds of government power in the face of rapid technological change.
An effective democratic society depends on the confidence citizens place in their government. Payment of taxes, acceptance of legislative and judicial decisions, compliance with social service programs, and support of military objectives are but some examples of the need for public cooperation with state demands. At the same time, voters expect their officials to behave ethically and responsibly. To those seeking to understand-and to improve-this mutual responsiveness, Trust and Governance provides a wide-ranging inquiry into the role of trust in civic life. Trust and Governance asks several important questions: Is trust really essential to good governance, or are strong laws more important? What leads people either to trust or to distrust government, and what makes officials decide to be trustworthy? Can too much trust render the public vulnerable to government corruption, and if so what safeguards are necessary? In approaching these questions, the contributors draw upon an abundance of historical and current resources to offer a variety of perspectives on the role of trust in government. For some, trust between citizens and government is a rational compact based on a fair exchange of information and the public's ability to evaluate government performance. Levi and Daunton each examine how the establishment of clear goals and accountability procedures within government agencies facilitates greater public commitment, evidence that a strong government can itself be a source of trust. Conversely, Jennings and Peel offer two cases in which loss of citizen confidence resulted from the administration of seemingly unresponsive, punitive social service programs. Other contributors to Trust and Governance view trust as a social bonding, wherein the public's emotional investment in government becomes more important than their ability to measure its performance. The sense of being trusted by voters can itself be a powerful incentive for elected officials to behave ethically, as Blackburn, Brennan, and Pettit each demonstrate. Other authors explore how a sense of communal identity and shared values make citizens more likely to eschew their own self-interest and favor the government as a source of collective good. Underlying many of these essays is the assumption that regulatory institutions are necessary to protect citizens from the worst effects of misplaced trust. Trust and Governance offers evidence that the jurisdictional level at which people and government interact-be it federal, state, or local-is fundamental to whether trust is rationally or socially based. Although social trust is more prevalent at the local level, both forms of trust may be essential to a healthy society. Enriched by perspectives from political science, sociology, psychology, economics, history, and philosophy, Trust and Governance opens a new dialogue on the role of trust in the vital relationship between citizenry and government.
'[Valerie] Braithwaite merges her considerable knowledge of a wide range of disciplines to produce an exemplar of interdisciplinary research. The use of the taxation system as the basis for analysis of how people manage their relationship with authority is effective and produces a much-needed addition to the behavioural literature. While the book is primarily about defiance in taxation, many instances of non-taxation related defiance are included, which provides excellent support and extension of the tax-based arguments. Braithwaite has produced an excellent example of a book that is grounded in the extant literature, while expanding our understanding of the importance of understanding the behaviours that drive defiance. The aim of the book is to "show how authorities can live symbiotically with defiance" and she achieves this superbly, illustrating how improved satisfaction with "the process" can minimise defiance.'
What happens when a person's commonsense view of justice diverges from the sense of justice he or she sees enshrined in particular laws? Does the perception of one particular law as unjust make an individual less likely to comply with unrelated laws? This Article advances the Flouting Thesis - the idea that the perceived legitimacy of one law or legal outcome can influence one's willingness to comply with unrelated laws-and provides original experimental evidence to support this thesis. The results suggest that willingness to disobey the law can extend far beyond the particular unjust law in question, to willingness to flout unrelated laws commonly encountered in everyday life (such as traffic violations, petty theft, and copyright restrictions), as well as willingness of mock jurors to engage in juror nullification. Finally, this Article explores the relationship between perceived injustice and flouting and offers several possible explanations, including the role of law in American popular culture and the expressive function of the law in producing compliance.
This paper investigates the relationship between making additional payments to the state for student loan (via the Higher Education Contribution Scheme) and child support (via the Child Support Scheme) and compliance with tax law. Data are taken from the Community Hopes, Fears, and Actions Survey based on a random sample of 2040 individuals. Additional payments were found to pose a compliance problem for tax authorities. At the same time, this study demonstrated that perceived deterrence, moral obligation and possible trustworthiness play significant roles in reducing tax evasion. An important finding to emerge from this study is that tax evasion is more likely to accompany additional payments when personal income and belief in trust norms are low. The finding of greater tax evasion among economically marginalized groups has been demonstrated in other contexts, but the adverse effects of becoming irreconcilably socially marginalized from legal authority has tended to be both undervalued and under-theorized in the taxation compliance literature.
Cash economy: summary of CTSI research findings and questions for future research Centre for Tax System Integrity Research Note 6 Australian National University
- V Braithwaite
Trust in electoral management bodies Australian National University
- T P Laanela
Submission No. 15 to the Senate Community Affairs References Committee Inquiry into Centrelink's compliance program
- D O'donovan
The Guardian Australia
- P. Karp
The Guardian Australia
- K. Murphy
Trends and Issues in Crime and Criminal Justice
- T. Prenzler
forthcoming)Trust in electoral management bodies
- T P Laanela
Report of the United Nations Special Rapporteur on extreme poverty and human rights: digital technology, social protection and human rights
- P. Alston
The Guardian Australia
- C. Knaus