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Artificial intelligence (AI) ethics is now a global topic of discussion in academic and policy circles. At least 84 public–private initiatives have produced statements describing high-level principles, values and other tenets to guide the ethical development, deployment and governance of AI. According to recent meta-analyses, AI ethics has seemingly converged on a set of principles that closely resemble the four classic principles of medical ethics. Despite the initial credibility granted to a principled approach to AI ethics by the connection to principles in medical ethics, there are reasons to be concerned about its future impact on AI development and governance. Significant differences exist between medicine and AI development that suggest a principled approach for the latter may not enjoy success comparable to the former. Compared to medicine, AI development lacks (1) common aims and fiduciary duties, (2) professional history and norms, (3) proven methods to translate principles into practice, and (4) robust legal and professional accountability mechanisms. These differences suggest we should not yet celebrate consensus around high-level principles that hide deep political and normative disagreement.

Do we have a right to transparency when we use content personalization systems? Building on prior work in discrimination detection in data mining, I propose algorithm auditing as a compatible ethical duty for providers of content personalization systems to maintain the transparency of political discourse. I explore barriers to auditing that reveal the practical limitations on the ethical duties of service providers. Content personalization systems can function opaquely and resist auditing. However, the belief that highly complex algorithms, such as bots using machine learning, are incomprehensible to human users should not be an excuse to surrender high quality political discourse. Auditing is recommended as a way to map and redress algorithmic political exclusion in practice. However, the opacity of algorithmic decision making poses a significant challenge to the implementation of auditing.

Online behaYioural adYertising (µOBA¶) relies on inferential anal\tics to target consumers based on data about their online behaviour. While the technology can improve the matching of adverts Zith consumers¶ preferences, it also poses risks to consumer welfare as consumers face offer discrimination and the exploitation of their cognitive errors. The technolog\¶s risks are exacerbated by the market power of ad intermediaries. This article shows how the Unfair Commercial Practices Directive (UCPD) can protect consumers from behavioural exploitation through incorporating market power analysis. By drawing on current research in economic theory, it argues for applying a stricter average consumer test if the market for ad intermediaries is highly concentrated. This stricter test should neutralize negative effects of behavioural targeting on consumer welfare. The article shows how OBA can amount to a misleading action and/or a misleading omission according to Articles 6 and 7 UCPD as well as an aggressive practice according to Article 8 UCPD. It further considers how the recent legislative proposals by the European Commission to enact a Digital Markets Act (DMA) and a Digital Services Act (DSA) may interact with the UCPD and the suggested stricter average consumer test.

In its attempt to better regulate the platform economy, the European Commission recently proposed a Digital Markets Act (DMA) and a Digital Services Act (DSA). While the DMA addresses worries about digital markets not functioning properly, the DSA is concerned with societal harms stemming from the dissemination of (illegal) content on platforms. Both proposals focus on the relative size of platforms. The DMA applies to 'gatekeeper' platforms and the DSA has a special regime of scrutiny for 'very large online platforms' (VLOPs). Focusing on size, however, can have negative consequences for the enforcement of the DSA: First, risks disseminated by platforms below the VLOP-threshold reside in a regulatory blind spot. Second, VLOPs may leverage their market power against their new mandatory auditors and risk assessors, a threat theorised as 'audit capture' in this article. As a result, societal risks may remain undiscovered or downplayed and consumers and citizens may be harmed. This article traces the origin of the size criteria in the legislative history of the DMA and DSA proposals. It argues for safeguards against audit capture and adverse incentive structures in the DSA. The article draws on the debate on audit reform in the aftermath of the global financial crisis of 2007–2008 to provide blueprints for fixing the regulatory gap.

Western societies are marked by diverse and extensive biases and inequality that are unavoidably embedded in the data used to train machine learning. Algorithms trained on biased data will, without intervention, produce biased outcomes and increase the inequality experienced by historically disadvantaged groups. Recognising this problem, much work has emerged in recent years to test for bias in machine learning and AI systems using various fairness and bias metrics. Often these metrics address technical bias but ignore the underlying causes of inequality. In this paper we make three contributions. First, we assess the compatibility of fairness metrics used in machine learning against the aims and purpose of EU non-discrimination law. We show that the fundamental aim of the law is not only to prevent ongoing discrimination, but also to change society, policies, and practices to 'level the playing field' and achieve substantive rather than merely formal equality. Based on this, we then propose a novel classification scheme for fairness metrics in machine learning based on how they handle pre-existing bias and thus align with the aims of non-discrimination law. Specifically, we distinguish between 'bias preserving' and 'bias transforming' fairness metrics. Our classification system is intended to bridge the gap between non-discrimination law and decisions around how to measure fairness in machine learning and AI in practice. Finally, we show that the legal need for justification in cases of indirect discrimination can impose additional obligations on developers, deployers, and users that choose to use bias preserving fairness metrics when making decisions about individuals because they can give rise to prima facie discrimination. To achieve substantive equality in practice, and thus meet the aims of the law, we instead recommend using bias transforming metrics. To conclude, we provide concrete recommendations including a user-friendly checklist for choosing the most appropriate fairness metric for uses of machine ...

In recent years a substantial literature has emerged concerning bias, discrimination, and fairness in artificial intelligence (AI) and machine learning. Connecting this work to existing legal non-discrimination frameworks is essential to create tools and methods that are practically useful across divergent legal regimes. While much work has been undertaken from an American legal perspective, comparatively little has mapped the effects and requirements of EU law. This Article addresses this critical gap between legal, technical, and organisational notions of algorithmic fairness. Through analysis of EU non-discrimination law and jurisprudence of the European Court of Justice (ECJ) and national courts, we identify a critical incompatibility between European notions of discrimination and existing work on algorithmic and automated fairness. A clear gap exists between statistical measures of fairness as embedded in myriad fairness toolkits and governance mechanisms and the context-sensitive, often intuitive and ambiguous discrimination metrics and evidential requirements used by the ECJ; we refer to this approach as "contextual equality." This Article makes three contributions. First, we review the evidential requirements to bring a claim under EU non-discrimination law. Due to the disparate nature of algorithmic and human discrimination, the EU's current requirements are too contextual, reliant on intuition, and open to judicial interpretation to be automated. Many of the concepts fundamental to bringing a claim, such as the composition of the disadvantaged and advantaged group, the severity and type of harm suffered, and requirements for the relevance and admissibility of evidence, require normative or political choices to be made by the judiciary on a case-by-case basis. We show that automating fairness or non-discrimination in Europe may be impossible because the law, by design, does not provide a static or homogenous framework suited to testing for discrimination in AI systems. Second, we show how the legal ...

The chapter undertakes a comparison of different approaches to the ethical assessment of novel technologies by looking at two recent research projects. ETICA was a FP7 sister project to PHM-Ethics, responsible for identification and ethical evaluation of information and communication technologies emerging in the next 10-15 years. The aims, methods, outcomes and recommendations of ETICA are compared to those of PHM-Ethics, with identification of linkages and similar findings. A relationship is identified between the two projects, in which the assessment methodologies developed in the projects are shown to operate at separate, but complementary levels. ETICA sought to reform EU ethics governance for emerging ICTs. The outcomes of PHM-Ethics are analyzed within the policy recommendations of ETICA, which demonstrate how the PHM-Ethics toolbox can contribute to ethics governance reform and context-sensitive ethical assessment of the sort called for by ETICA.

Since approval of the EU General Data Protection Regulation (GDPR) in 2016, it has been widely and repeatedly claimed that the GDPR will legally mandate a 'right to explanation' of all decisions made by automated or artificially intelligent algorithmic systems. This right to explanation is viewed as an ideal mechanism to enhance the accountability and transparency of automated decision-making. However, there are several reasons to doubt both the legal existence and the feasibility of such a right. In contrast to the right to explanation of specific automated decisions claimed elsewhere, the GDPR only mandates that data subjects receive meaningful, but properly limited, information (Articles 13-15) about the logic involved, as well as the significance and the envisaged consequences of automated decision-making systems, what we term a 'right to be informed'. Further, the ambiguity and limited scope of the 'right not to be subject to automated decision-making' contained in Article 22 (from which the alleged 'right to explanation' stems) raises questions over the protection actually afforded to data subjects. These problems show that the GDPR lacks precise language as well as explicit and well-defined rights and safeguards against automated decision-making, and therefore runs the risk of being toothless. We propose a number of legislative and policy steps that, if taken, may improve the transparency and accountability of automated decision-making when the GDPR comes into force in 2018.

In information societies, operations, decisions and choices previously left to humans are increasingly delegated to algorithms, which may advise, if not decide, about how data should be interpreted and what actions should be taken as a result. More and more often, algorithms mediate social processes, business transactions, governmental decisions, and how we perceive, understand, and interact among ourselves and with the environment. Gaps between the design and operation of algorithms and our understanding of their ethical implications can have severe consequences affecting individuals as well as groups and whole societies. This paper makes three contributions to clarify the ethical importance of algorithmic mediation. It provides a prescriptive map to organise the debate. It reviews the current discussion of ethical aspects of algorithms. And it assesses the available literature in order to identify areas requiring further work to develop the ethics of algorithms.

In October 2016, the White House, the European Parliament, and the UK House of Commons each issued a report outlining their visions on how to prepare society for the widespread use of artificial intelligence (AI). In this article, we provide a comparative assessment of these three reports in order to facilitate the design of policies favourable to the development of a 'good AI society'. To do so, we examine how each report addresses the following three topics: (a) the development of a 'good AI society'; (b) the role and responsibility of the government, the private sector, and the research community (including academia) in pursuing such a development; and (c) where the recommendations to support such a development may be in need of improvement. Our analysis concludes that the reports address adequately various ethical, social, and economic topics, but come short of providing an overarching political vision and long-term strategy for the development of a 'good AI society'. In order to contribute to fill this gap, in the conclusion we suggest a two-pronged approach.

Objective: Health outcome prioritisation is the ranking in order of desirability or importance of a set of disease related objectives and their associated cost or risk. We analyse the complex ethical landscape in which this takes place in the most common dementia, Alzheimer's disease. Background: Dementia has been described as the greatest global health challenge in the 21st century on account of longevity gains increasing its incidence, escalating health and social care pressures. These pressures highlight ethical, social, political challenges about healthcare resource allocation, what health improvements matter to patients, and how they are measured. This study highlights the complexity of the ethical landscape, relating particularly to the balances that need to be struck when allocating resources; when measuring and prioritising outcomes; and when individual preferences are sought. Methods: Narrative review of literature published since 2007, incorporating snowball sampling where necessary. We identified, thematised and discussed key issues of ethical salience. Results: Eight areas of ethical salience for outcome prioritisation emerged: (1) Public health and distributive justice, (2) Scarcity of resources, (3) Heterogeneity and changing circumstances, (4) Knowledge of treatment, (5) Values and circumstances, (6) Conflicting priorities, (7) Communication, autonomy and Caregiver issues, (8) Disclosure of risk. Conclusion: These areas highlight the difficult balance to be struck when allocating resources, when measuring and prioritising outcomes, and when individual preferences are sought. We conclude by reflecting on how tools in social sciences and ethics can help address challenges posed by resource allocation, measuring and prioritising outcomes, and eliciting stakeholder preferences.

This report examines the policy challenges for the Internet of Things (IoT), and raises a broad range of issues that need to be considered if policy is to be effective and the potential economic value of IoT is harnessed. It builds on the Blackett review, The Internet of Things: making the most of the second digital revolution, adding detailed knowledge based on research from the PETRAS Cybersecurity of the Internet of Things Research Hub and input from Fellows of the Royal Academy of Engineering. The report targets government policymakers, regulators, standards bodies and national funding bodies, and will also be of interest to suppliers and adopters of IoT products and services.