The Role and Limits of Principles in AI Ethics: Towards a Focus on Tensions

The last few years have seen a proliferation of principles for AI ethics. There is substantial overlap between different sets of principles, with widespread agreement that AI should be used for the common good, should not be used to harm people or undermine their rights, and should respect widely held values such as fairness, privacy, and autonomy. While articulating and agreeing on principles is important, it is only a start- ing point. Drawing on comparisons with the field of bioethics, we highlight some of the limitations of principles: in particular, they are often too broad and high-level to guide ethics in practice. We suggest that an important next step for the field of AI ethics is to focus on exploring the tensions that inevitably arise as we try to implement principles in practice. By explicitly recognising these tensions we can begin to make decisions about how they should be resolved in specific cases, and develop frameworks and guidelines for AI ethics that are rigorous and practically relevant. We discuss some different specific ways that tensions arise in AI ethics, and what processes might be needed to resolve them.Work supported by the Nuffield Foundation and Leverhulme Trus

Creating Fair Models of Atherosclerotic Cardiovascular Disease Risk

International audienceGuidelines for the management of atherosclerotic cardiovascular disease (ASCVD) recommend theuse of risk stratification models to identify patients most likely to benefit from cholesterol-loweringand other therapies. These models have differential performance across race and gender groups withinconsistent behavior across studies, potentially resulting in an inequitable distribution of beneficialtherapy. In this work, we leverage adversarial learning and a large observational cohort extractedfrom electronic health records (EHRs) to develop a "fair" ASCVD risk prediction model with reducedvariability in error rates across groups. We empirically demonstrate that our approach is capableof aligning the distribution of risk predictions conditioned on the outcome across several groupssimultaneously for models built from high-dimensional EHR data. We also discuss the relevance ofthese results in the context of the empirical trade-off between fairness and model performance

'Scary Robots': Examining public responses to AI

How AI is perceived by the public can have significant im-pact on how it is developed, deployed and regulated. Some commentators argue that perceptions are currently distorted or extreme. This paper discusses the results of a nationally representative survey of the UK population on their percep-tions of AI. The survey solicited responses to eight common narratives about AI (four optimistic, four pessimistic), plus views on what AI is, how likely it is to impact in respond-ents’ lifetimes, and whether they can influence it. 42% of respondents offered a plausible definition of AI, while 25% thought it meant robots. Of the narratives presented, those associated with automation were best known, followed by the idea that AI would become more powerful than humans. Overall results showed that the most common visions of the impact of AI elicit significant anxiety. Only two of the eight narratives elicited more excitement than concern (AI making life easier, and extending life). Respondents felt they had no control over AI’s development, citing the power of corpora-tions or government, or versions of technological determin-ism. Negotiating the deployment of AI will require contend-ing with these anxieties.Drs Cave and Dihal are funded by a Leverhulme Trust Research Centre Grant awarded to the Leverhulme Centre for the Future of Intelligenc

Explainability in AI Policies: A Critical Review of Communications, Reports, Regulations, and Standards in the EU, US, and UK

Public attention towards explainability of artificial intelligence (AI) systems has been rising in recent years to offer methodologies for human oversight. This has translated into the proliferation of research outputs, such as from Explainable AI, to enhance transparency and control for system debugging and monitoring, and intelligibility of system process and output for user services. Yet, such outputs are difficult to adopt on a practical level due to a lack of a common regulatory baseline, and the contextual nature of explanations. Governmental policies are now attempting to tackle such exigence, however it remains unclear to what extent published communications, regulations, and standards adopt an informed perspective to support research, industry, and civil interests. In this study, we perform the first thematic and gap analysis of this plethora of policies and standards on explainability in the EU, US, and UK. Through a rigorous survey of policy documents, we first contribute an overview of governmental regulatory trajectories within AI explainability and its sociotechnical impacts. We find that policies are often informed by coarse notions and requirements for explanations. This might be due to the willingness to conciliate explanations foremost as a risk management tool for AI oversight, but also due to the lack of a consensus on what constitutes a valid algorithmic explanation, and how feasible the implementation and deployment of such explanations are across stakeholders of an organization. Informed by AI explainability research, we then conduct a gap analysis of existing policies, which leads us to formulate a set of recommendations on how to address explainability in regulations for AI systems, especially discussing the definition, feasibility, and usability of explanations, as well as allocating accountability to explanation providers

Normative Ethics Principles for Responsible AI Systems: Taxonomy and Future Directions

The rapid adoption of artificial intelligence (AI) necessitates careful analysis of its ethical implications. In addressing ethics and fairness implications, it is important to examine the whole range of ethically relevant features rather than looking at individual agents alone. This can be accomplished by shifting perspective to the systems in which agents are embedded, which is encapsulated in the macro ethics of sociotechnical systems (STS). Through the lens of macro ethics, the governance of systems - which is where participants try to promote outcomes and norms which reflect their values - is key. However, multiple-user social dilemmas arise in an STS when stakeholders of the STS have different value preferences or when norms in the STS conflict. To develop equitable governance which meets the needs of different stakeholders, and resolve these dilemmas in satisfactory ways with a higher goal of fairness, we need to integrate a variety of normative ethical principles in reasoning. Normative ethical principles are understood as operationalizable rules inferred from philosophical theories. A taxonomy of ethical principles is thus beneficial to enable practitioners to utilise them in reasoning. This work develops a taxonomy of normative ethical principles which can be operationalized in the governance of STS. We identify an array of ethical principles, with 25 nodes on the taxonomy tree. We describe the ways in which each principle has previously been operationalized, and suggest how the operationalization of principles may be applied to the macro ethics of STS. We further explain potential difficulties that may arise with each principle. We envision this taxonomy will facilitate the development of methodologies to incorporate ethical principles in reasoning capacities for governing equitable STS

Theories of parenting and their application to artificial intelligence

© 2019 Copyright is held by the owner/author(s). As machine learning (ML) systems have advanced, they have acquired more power over humans' lives, and questions about what values are embedded in them have become more complex and fraught. It is conceivable that in the coming decades, humans may succeed in creating artificial general intelligence (AGI) that thinks and acts with an open-endedness and autonomy comparable to that of humans. The implications would be profound for our species; they are now widely debated not just in science fiction and speculative research agendas but increasingly in serious technical and policy conversations. Much work is underway to try to weave ethics into advancing ML research. We think it useful to add the lens of parenting to these efforts, and specifically radical, queer theories of parenting that consciously set out to nurture agents whose experiences, objectives and understanding of the world will necessarily be very different from their parents'. We propose a spectrum of principles which might underpin such an effort; some are relevant to current ML research, while others will become more important if AGI becomes more likely. These principles may encourage new thinking about the development, design, training, and release into the world of increasingly autonomous agents