This article reviews the academic literature that emerged under value sensitive design. It investigates those VSD projects that employed the tripartite methodology, examining the use of VSD methodological elements, and illustrating common practices and identifying shortcomings. The article provides advice for VSD researchers on how to complete and enhance their methodological approach as the research community moves forward.

It should not be a surprise in the near future to encounter either a personal or a professional service robot in our homes and/or our work places: according to the International Federation for Robots, there will be approx 35 million service robots at work by 2018. Given that individuals will interact and even cooperate with these service robots, their design and development demand ethical attention. With this in mind I suggest the use of an approach for incorporating ethics into the (...) design process of robots known as Care Centered Value Sensitive Design. Although this approach was originally and intentionally designed for the healthcare domain, the aim of this paper is to present a preliminary study of how personal and professional service robots might also be evaluated using the CCVSD approach. The normative foundations for CCVSD come from its reliance on the care ethics tradition and in particular the use of care practices for: structuring the analysis and, determining the values of ethical import. To apply CCVSD outside of healthcare one must show that the robot has been integrated into a care practice. Accordingly, the practice into which the robot is to be used must be assessed and shown to meet the conditions of a care practice. By investigating the foundations of the approach I hope to show why it may be applicable for service robots and further to give examples of current robot prototypes that can and cannot be evaluated using CCVSD. (shrink)

Some philosophers have argued that during the process of obtaining informed consent, physicians should try to nudge their patients towards consenting to the option the physician believes best, where a nudge is any influence that is expected to predictably alter a person’s behaviour without restricting her options. Some proponents of nudging even argue that it is a necessary and unavoidable part of securing informed consent. Here I argue that nudging is incompatible with obtaining informed consent. I assume informed consent requires (...) that a physician tells her patient the truth about her options and argue that nudging is incompatible with truth-telling. Instead, nudging satisfies Harry Frankfurt’s account of bullshit. (shrink)

The datafication and digitalization of health and medicine has engendered a proliferation of new collaborations between public health institutions and data corporations like Google, Apple, Microsoft and Amazon. Critical perspectives on these new partnerships tend to frame them as an instance of market transgressions by tech giants into the sphere of health and medicine, in line with a “hostile worlds” doctrine that upholds that the borders between market and non-market spheres should be carefully policed. This article seeks to outline the (...) limitations of this common framing for critically understanding the phenomenon of the Googlization of health. In particular, the mobilization of a diversity of non-market value statements in the justification work carried out by actors involved in the Googlization of health indicates the co-presence of additional worlds or spheres in this context, which are not captured by the market vs. non-market dichotomy. It then advances an alternative framework, based on a multiple-sphere ontology that draws on Boltanski and Thevenot’s orders of worth and Michael Walzer’s theory of justice, which I call a normative pragmatics of justice. This framework addresses both the normative deficit in Boltanski and Thevenot’s work and provides an important emphasis on the empirical workings of justice. Finally, I discuss why this framework is better equipped to identify and to address the many risks raised by the Googlization of health and possibly other dimensions of the digitalization and datafication of society. (shrink)

Public health policies often require individuals to make personal sacrifices for the sake of protecting other individuals or the community at large. Such requirements can be more or less demanding for individuals. This paper examines the implications of demandingness for public health ethics and policy. It focuses on three possible public health policies that pose requirements that are differently demanding: vaccination policies, policy to contain antimicrobial resistance, and quarantine and isolation policies. Assuming the validity of the ‘demandingness objection’ in ethics, (...) we argue that states should try to pose requirements that individuals would have an independent moral obligation to fulfil, and therefore that are not too demanding. In such cases, coercive measures are ethically justified, especially if the interventions also entail some benefits to the individuals; this is, for example, the case of vaccination policies. When public health policies need to require individuals to do something that is too demanding to constitute an independent moral obligation, states have an obligation to either provide incentives to give individuals non-moral reasons to fulfil a certain requirement – as in the case of policies that limit antibiotic prescriptions – or to compensate individuals for being forced to do something that is too demanding to constitute an independent moral obligation – as in the case of quarantine and isolation policies. (shrink)

The notion of “responsibility gap” with artificial intelligence (AI) was originally introduced in the philosophical debate to indicate the concern that “learning automata” may make more difficult or impossible to attribute moral culpability to persons for untoward events. Building on literature in moral and legal philosophy, and ethics of technology, the paper proposes a broader and more comprehensive analysis of the responsibility gap. The responsibility gap, it is argued, is not one problem but a set of at least four interconnected (...) problems – gaps in culpability, moral and public accountability, active responsibility—caused by different sources, some technical, other organisational, legal, ethical, and societal. Responsibility gaps may also happen with non-learning systems. The paper clarifies which aspect of AI may cause which gap in which form of responsibility, and why each of these gaps matter. It proposes a critical review of partial and non-satisfactory attempts to address the responsibility gap: those which present it as a new and intractable problem (“fatalism”), those which dismiss it as a false problem (“deflationism”), and those which reduce it to only one of its dimensions or sources and/or present it as a problem that can be solved by simply introducing new technical and/or legal tools (“solutionism”). The paper also outlines a more comprehensive approach to address the responsibility gaps with AI in their entirety, based on the idea of designing socio-technical systems for “meaningful human control", that is systems aligned with the relevant human reasons and capacities. (shrink)

In an analysis of recent work on nudging we distinguish three positions on the relationship between nudging founded in libertarian paternalism and the protection of personal autonomy through informed consent. We argue that all three positions fail to provide adequate protection of personal autonomy in the clinical context. Acknowledging that nudging may be beneficial, we suggest a fourth position according to which nudging and informed consent are valuable in different domains of interaction.

There have been calls for some time for a new approach to public health in the United Kingdom and beyond. This is consequent on the recognition and acceptance that health problems often have a complex and multi-faceted aetiology. At the same time, policies which utilise insights from research in behavioural economics and psychology have gained prominence on the political agenda. The relationship between the social determinants of health and behavioural science in health policy has not hitherto been explored. Given the (...) on-going presence of strategies based on findings from behavioural science in policy-making on the political agenda, an examination of this is warranted. This paper begins by looking at the place of the SDoH within public health, before outlining, in brief, the recent drive towards utilising behavioural science to formulate law and public policy. We then examine the relationship between this and the SDoH. We argue that behavioural public health policy is, to a certain extent, blind to the social and other determinants of health. In section three, we examine ways in which such policies may perpetuate and/or exacerbate health inequities and social injustices. We argue that problems in this respect may be compounded by assumptions and practices which are built into some behavioural science methodologies. We also argue that incremental individual gains may not be enough. As such, population-level measures are sometimes necessary. In section four we defend this contention, arguing that an equitable and justifiable public health requires such measures. (shrink)

Although decision‐making algorithms are not new to medicine, the availability of vast stores of medical data, gains in computing power, and breakthroughs in machine learning are accelerating the pace of their development, expanding the range of questions they can address, and increasing their predictive power. In many cases, however, the most powerful machine learning techniques purchase diagnostic or predictive accuracy at the expense of our ability to access “the knowledge within the machine.” Without an explanation in terms of reasons or (...) a rationale for particular decisions in individual cases, some commentators regard ceding medical decision‐making to black box systems as contravening the profound moral responsibilities of clinicians. I argue, however, that opaque decisions are more common in medicine than critics realize. Moreover, as Aristotle noted over two millennia ago, when our knowledge of causal systems is incomplete and precarious—as it often is in medicine—the ability to explain how results are produced can be less important than the ability to produce such results and empirically verify their accuracy. (shrink)

In this article we argue that a widespread adoption of nudging can alter legal and political institutions. Debates on nudges thus far have largely revolved around a set of philosophical theories that we call individualistic approaches. Our analysis concerns the ways in which adherents of nudging make use of the newest findings in the behavioral sciences for the purposes of policy-making. We emphasize the fact that most nudges proposed so far are not a part of the legal system and are (...) also non-normative. We propose two ideal types: “law-as-normative” and “law-as-instrumental”, that allow us to understand and evaluate the relation of nudges and the law. We stress the importance of law as a safeguard for the possible negative consequences of nudges and conclude with proposals that could complement nudging policies. (shrink)

The idea of artificial intelligence for social good is gaining traction within information societies in general and the AI community in particular. It has the potential to tackle social problems through the development of AI-based solutions. Yet, to date, there is only limited understanding of what makes AI socially good in theory, what counts as AI4SG in practice, and how to reproduce its initial successes in terms of policies. This article addresses this gap by identifying seven ethical factors that are (...) essential for future AI4SG initiatives. The analysis is supported by 27 case examples of AI4SG projects. Some of these factors are almost entirely novel to AI, while the significance of other factors is heightened by the use of AI. From each of these factors, corresponding best practices are formulated which, subject to context and balance, may serve as preliminary guidelines to ensure that well-designed AI is more likely to serve the social good. (shrink)

This article reports the findings of AI4People, an Atomium—EISMD initiative designed to lay the foundations for a “Good AI Society”. We introduce the core opportunities and risks of AI for society; present a synthesis of five ethical principles that should undergird its development and adoption; and offer 20 concrete recommendations—to assess, to develop, to incentivise, and to support good AI—which in some cases may be undertaken directly by national or supranational policy makers, while in others may be led by other (...) stakeholders. If adopted, these recommendations would serve as a firm foundation for the establishment of a Good AI Society. (shrink)

Interactions between an intelligent software agent and a human user are ubiquitous in everyday situations such as access to information, entertainment, and purchases. In such interactions, the ISA mediates the user’s access to the content, or controls some other aspect of the user experience, and is not designed to be neutral about outcomes of user choices. Like human users, ISAs are driven by goals, make autonomous decisions, and can learn from experience. Using ideas from bounded rationality, we frame these interactions (...) as instances of an ISA whose reward depends on actions performed by the user. Such agents benefit by steering the user’s behaviour towards outcomes that maximise the ISA’s utility, which may or may not be aligned with that of the user. Video games, news recommendation aggregation engines, and fitness trackers can all be instances of this general case. Our analysis facilitates distinguishing various subcases of interaction, as well as second-order effects that might include the possibility for adaptive interfaces to induce behavioural addiction, and/or change in user belief. We present these types of interaction within a conceptual framework, and review current examples of persuasive technologies and the issues that arise from their use. We argue that the nature of the feedback commonly used by learning agents to update their models and subsequent decisions could steer the behaviour of human users away from what benefits them, and in a direction that can undermine autonomy and cause further disparity between actions and goals as exemplified by addictive and compulsive behaviour. We discuss some of the ethical, social and legal implications of this technology and argue that it can sometimes exploit and reinforce weaknesses in human beings. (shrink)

Public health ethicists face two difficult questions. First, what makes something a matter of public health? While protecting citizens from outbreaks of communicable diseases is clearly a matter of public health, is the same true of policies that aim to reduce obesity, gun violence or political corruption? Second, what should the scope of the government’s authority be in promoting public health? May government enact public health policies some citizens reasonably object to or policies that are paternalistic? Recently, some theorists have (...) attempted to address these questions by arguing that something is a matter of public health if and only if it involves a health-related public good, such as clean water or herd immunity. Relatedly, they have argued that appeals to the promotion of public health should only be used to justify the provision of health-related public goods. This public goods conception of public health (PGC) is meant to enjoy advantages over its rivals in three respects: it provides a better definition of public health than rival views, it respects moral disagreement, and it avoids licensing objectionably paternalistic public health policies. We argue, however, that the PGC does just as poorly, or worse, than its rivals in all three respects. (shrink)

The last decade has witnessed the mass distribution and adoption of smart home systems and devices powered by artificial intelligence systems ranging from household appliances like fridges and toasters to more background systems such as air and water quality controllers. The pervasiveness of these sociotechnical systems makes analyzing their ethical implications necessary during the design phases of these devices to ensure not only sociotechnical resilience, but to design them for human values in mind and thus preserve meaningful human control over (...) them. This paper engages in a conceptual investigations of how meaningful human control over smart home devices can be attained through design. The value sensitive design (VSD) approach is proposed as a way of attaining this level of control. In the proposed framework, values are identified and defined, stakeholder groups are investigated and brought into the design process and the technical constraints of the technologies in question are considered. The paper concludes with some initial examples that illustrate a more adoptable way forward for both ethicists and engineers of smart home devices. (shrink)

The last decade has witnessed the mass distribution and adoption of smart home systems and devices powered by artificial intelligence systems ranging from household appliances like fridges and toasters to more background systems such as air and water quality controllers. The pervasiveness of these sociotechnical systems makes analyzing their ethical implications necessary during the design phases of these devices to ensure not only sociotechnical resilience, but to design them for human values in mind and thus preserve meaningful human control over (...) them. This paper engages in a conceptual investigations of how meaningful human control over smart home devices can be attained through design. The value sensitive design (VSD) approach is proposed as a way of attaining this level of control. In the proposed framework, values are identified and defined, stakeholder groups are investigated and brought into the design process and the technical constraints of the technologies in question are considered. The paper concludes with some initial examples that illustrate a more adoptable way forward for both ethicists and engineers of smart home devices. (shrink)

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. (shrink)

Value Sensitive Design (VSD) is an established method for integrating values into technical design. It has been applied to different technologies and, more recently, to artificial intelligence (AI). We argue that AI poses a number of challenges specific to VSD that require a somewhat modified VSD approach. Machine learning (ML), in particular, poses two challenges. First, humans may not understand how an AI system learns certain things. This requires paying attention to values such as transparency, explicability, and accountability. Second, ML (...) may lead to AI systems adapting in ways that ‘disembody’ the values embedded in them. To address this, we propose a threefold modified VSD approach: 1) integrating a known set of VSD principles (AI4SG) as design norms from which more specific design requirements can be derived; 2) distinguishing between values that are promoted and respected by the design to ensure outcomes that not only do no harm but also contribute to good; and 3) extending the VSD process to encompass the whole life cycle of an AI technology in order to monitor unintended value consequences and redesign as needed. We illustrate our VSD for AI approach with an example use case of a SARS-CoV-2 contact tracing app. (shrink)