Rowena Rodrigues’s research while affiliated with Trilateral Research and other places

Research misconduct (RM) and questionable research practices (QRPs) have a considerable impact on researchers, economy and society. Using a socio-economic impact assessment methodology, this article identifies and assesses their impacts. The objective is to help support the measures developed to promote research ethics and research integrity principles through shared responsibility (individual and institutional) and improve education and training. The article presents recommendations for policy and future research as part of a cohesive framework that takes socio-economic impacts into account. This qualitative study advances and updates current knowledge on the impacts of RM, enriching existing research by introducing new insights, especially regarding socio-economic dimensions, affected stakeholders, and the relevance and significance of these impacts.

Diverse initiatives promote the responsible development, deployment and use of Artificial Intelligence (AI). AI incident databases have emerged as a valuable and timely learning resource and tool in AI governance. This article assesses the value of such databases and outlines how this value can be enhanced. It reviews four databases: the AI Incident Database, the AI, Algorithmic, and Automation Incidents and Controversies Repository, the AI Incident Tracker and Where in the World Is AI. The article provides a descriptive analysis of these databases, examines their objectives, and locates them within the landscape of initiatives that advance responsible AI. It reflects on their primary objective, i.e. learning from mistakes to avoid them in the future, and explores how they might benefit diverse stakeholders. The article supports the broader uptake of these databases and recommends four key actions to enhance their value.

Artificial intelligence (AI) is producing highly beneficial impacts in many domains, from transport to healthcare, from energy distribution to marketing, but it also raises concerns about undesirable ethical and social consequences. AI impact assessments (AI-IAs) are a way of identifying positive and negative impacts early on to safeguard AI’s benefits and avoid its downsides. This article describes the first systematic review of these AI-IAs. Working with a population of 181 documents, the authors identified 38 actual AI-IAs and subjected them to a rigorous qualitative analysis with regard to their purpose, scope, organisational context, expected issues, timeframe, process and methods, transparency and challenges. The review demonstrates some convergence between AI-IAs. It also shows that the field is not yet at the point of full agreement on content, structure and implementation. The article suggests that AI-IAs are best understood as means to stimulate reflection and discussion concerning the social and ethical consequences of AI ecosystems. Based on the analysis of existing AI-IAs, the authors describe a baseline process of implementing AI-IAs that can be implemented by AI developers and vendors and that can be used as a critical yardstick by regulators and external observers to evaluate organisations’ approaches to AI.

Privacy and data protection are concerns raised about most digital technologies. The advance of artificial intelligence (AI) has given even higher levels of prominence to these concerns. Three cases are presented as examples to highlight the way in which AI can affect or exacerbate privacy concerns. The first deals with the use of private data in authoritarian regimes. The second looks at the implications of AI use of genetic data. The third concerns problems linked to biometric surveillance. Then follows a description of how privacy concerns are currently addressed via data protection regulation and a discussion of where AI may raise new challenges to existing data protection regimes. Current European data protection law requires data protection impact assessment. This chapter suggests that a broader AI impact assessment could broaden the remit of such an assessment to offer more comprehensive coverage of possible privacy concerns linked to AI.

Dignity is a very prominent concept in human rights instruments, in particular constitutions. It is also a concept that has many critics, including those who argue that it is useless in ethical debates. How useful or not dignity can be in artificial intelligence (AI) ethics discussions is the question of this chapter. Is it a conversation stopper, or can it help explain or even resolve some of the ethical dilemmas related to AI? The three cases in this chapter deal with groundless dismissal by an automated system, sex robotsand care robots. The conclusion argues that it makes perfect sense for human rights proponents to treat dignity as a prime value, which takes precedence over others in the case of extreme dignity violations such as torture, human trafficking, slavery and reproductive manipulation. However, in AI ethics debates, it is better seen as an equal among equals, so that the full spectrum of potential benefits and harms are considered for AI technologies using all relevant ethical values.

The concern that artificial intelligence (AI) can be used to manipulate individuals, with undesirable consequences for the manipulated individual as well as society as a whole, plays a key role in the debate on the ethics of AI. This chapter uses the case of the political manipulation of voters and that of the manipulation of vulnerable consumers as studies to explore how AI can contribute to and facilitate manipulation and how such manipulation can be evaluated from an ethical perspective. The chapter presents some proposed ways of dealing with the ethics of manipulation with reference to data protection, privacyand transparency in the of use of data. Manipulation is thus an ethical issue of AI that is closely related to other issues discussed in this book.

In 2015, 193 nations came together to agree Agenda 2030: 17 goals ranging from the elimination of poverty to the building of partnerships to achieve those goals. The spirit of the UN Sustainable Development Goals (SDGs) is to leave no one behind. Artificial intelligence (AI) has a great potential to assist in reaching the SDGs. For instance, using algorithms on new and vast agricultural data sets can improve the efficiency of agriculture practices and thereby contribute to SDG 1, “Zero hunger”. However, the high energy consumption, computational resources and levels of expertise required for AI can exacerbate existing inequalities. At the same time, potentially useful AI applications such as seasonal climate forecasting have led to the accelerated laying off of workers in Peru and credit denial to poor farmers in Zimbabweand Brazil. If AI for Good is to be truly realised, AI’s potential to worsen inequality, to overexploit resources, to be undertaken through “helicopter research” and to focus on SDG issues relevant mainly to high-income countries must be overcome, ideally in close collaboration and engagement with potential beneficiaries in resource-limited settings.

There is much debate about theUnfair discrimination ways in which artificial intelligenceArtificial intelligence (AI) systems can include and perpetuate biases and lead to unfair and often illegalIllegal discrimination against individualsIndividual on the basis of protected characteristics, such as age, race, gender and disabilityDisabilities. This chapter describes three cases of such discriminationDiscrimination. It starts with an account of the use of AIArtificial intelligence in hiring decisions that led to discrimination based on gender. The second case explores the way in which AI can lead to discrimination when applied in law enforcementLaw enforcement. The final example looks at implications of bias in the detection of skin colour. The chapter then discusses why these cases are considered to be ethical issues and how this ethics debate relates to well-established legislation around discriminationDiscrimination. The chapter proposes two ways of raising awareness of possible discriminatory characteristics of AI systems and ways of dealing with them: AI impact assessmentsImpact assessment and ethics by design.