The use of artificial intelligence (AI) to make high-stakes decisions is sometimes thought to create a troubling responsibility gap – that is, a situation where nobody can be held morally responsible for the outcomes that are brought about. However, philosophers and practitioners have recently claimed that, even though no individual can be held morally responsible, groups of individuals might be. Consequently, they think, we have less to fear from the use of AI than might appear to be the case. This (...) paper assesses this claim. Drawing on existing philosophical models of collective responsibility, I consider whether changing focus from the individual to the collective level can help us identify a locus of responsibility in a greater range of cases of AI deployment. I find that appeal to collective responsibility will be of limited use in filling the responsibility gap: the models considered either do not apply to the case at hand or else the relevant sort of collective responsibility, even if present, will not be sufficient to remove the costs that are often associated with an absence of responsibility. (shrink)

Artificial intelligence (AI) is becoming increasingly influential in most people’s lives. This raises many philosophical questions. One is what responsibility we have as individuals to guide the development of AI in a desirable direction. More specifically, how should this responsibility be distributed among individuals and between individuals and other actors? We investigate this question from the perspectives of five principles of distribution that dominate the discussion about responsibility in connection with climate change: effectiveness, equality, desert, need, and ability. Since much (...) is already written about these distributions in that context, we believe much can be gained if we can make use of this discussion also in connection with AI. Our most important findings are: (1) Different principles give different answers depending on how they are interpreted but, in many cases, different interpretations and different principles agree and even strengthen each other. If for instance ‘equality-based distribution’ is interpreted in a consequentialist sense, effectiveness, and through it, ability, will play important roles in the actual distributions, but so will an equal distribution as such, since we foresee that an increased responsibility of underrepresented groups will make the risks and benefits of AI more equally distributed. The corresponding reasoning is true for need-based distribution. (2) If we acknowledge that someone has a certain responsibility, we also have to acknowledge a corresponding degree of influence for that someone over the matter in question. (3) Independently of which distribution principle we prefer, ability cannot be dismissed. Ability is not fixed, however and if one of the other distributions is morally required, we are also morally required to increase the ability of those less able to take on the required responsibility. (shrink)

What is techno-optimism and how can it be defended? Although techno-optimist views are widely espoused and critiqued, there have been few attempts to systematically analyse what it means to be a techno-optimist and how one might defend this view. This paper attempts to address this oversight by providing a comprehensive analysis and evaluation of techno-optimism. It is argued that techno-optimism is a pluralistic stance that comes in weak and strong forms. These vary along a number of key dimensions but each (...) shares the view that technology plays a key role in ensuring that the good prevails over the bad. Whatever its strength, to defend this stance, one must flesh out an argument with four key premises. Each of these premises is highly controversial and can be subjected to a number of critiques. The paper discusses five such critiques in detail. The paper also considers possible responses from the techno-optimist. Finally, it is concluded that although strong forms of techno-optimism are not intellectually defensible, a modest, agency-based version of techno-optimism may be defensible. (shrink)

There is a concern that the widespread deployment of autonomous machines will open up a number of ‘responsibility gaps’ throughout society. Various articulations of such techno-responsibility gaps have been proposed over the years, along with several potential solutions. Most of these solutions focus on ‘plugging’ or ‘dissolving’ the gaps. This paper offers an alternative perspective. It argues that techno-responsibility gaps are, sometimes, to be welcomed and that one of the advantages of autonomous machines is that they enable us to embrace (...) certain kinds of responsibility gap. The argument is based on the idea that human morality is often tragic. We frequently confront situations in which competing moral considerations pull in different directions and it is impossible to perfectly balance these considerations. This heightens the burden of responsibility associated with our choices. We cope with the tragedy of moral choice in different ways. Sometimes we delude ourselves into thinking the choices we make were not tragic ; sometimes we delegate the tragic choice to others ; sometimes we make the choice ourselves and bear the psychological consequences. Each of these strategies has its benefits and costs. One potential advantage of autonomous machines is that they enable a reduced cost form of delegation. However, we only gain the advantage of this reduced cost if we accept that some techno-responsibility gaps are virtuous. (shrink)

The paper has two goals. The first is presenting the main results of the recent report Ethics of Connected and Automated Vehicles: recommendations on road safety, privacy, fairness, explainability and responsibility written by the Horizon 2020 European Commission Expert Group to advise on specific ethical issues raised by driverless mobility, of which the author of this paper has been member and rapporteur. The second is presenting some broader ethical and philosophical implications of these recommendations, and using these to contribute to (...) the establishment of Ethics of Transportation as an independent branch of applied ethics. The recent debate on the ethics of Connected and Automated Vehicles presents a paradox and an opportunity. The paradox is the presence of a flourishing debate on the ethics of one very specific transportation technology without ethics of transportation being in itself a well-established academic discipline. The opportunity is that now that a spotlight has been switched on the ethical dimensions of CAVs it may be easier to establish a broader debate on ethics of transportation. While the 20 recommendations of the EU report are grouped in three macro-areas: road safety, data ethics, and responsibility, in this paper they will be grouped according to eight philosophical themes: Responsible Innovation, road justice, road safety, freedom, human control, privacy, data fairness, responsibility. These are proposed as the first topics for a new ethics of transportation. (shrink)

By mid-2019 there were more than 80 AI ethics guides available in the public domain. Despite this, 2020 saw numerous news stories break related to ethically questionable uses of AI. In part, this is because AI ethics theory remains highly abstract, and of limited practical applicability to those actually responsible for designing algorithms and AI systems. Our previous research sought to start closing this gap between the ‘what’ and the ‘how’ of AI ethics through the creation of a searchable typology (...) of tools and methods designed to translate between the five most common AI ethics principles and implementable design practices. Whilst a useful starting point, that research rested on the assumption that _all_ AI practitioners are aware of the ethical implications of AI, understand their importance, and are actively seeking to respond to them. In reality, it is unclear whether this is the case. It is this limitation that we seek to overcome here by conducting a mixed-methods qualitative analysis to answer the following four questions: what do AI practitioners understand about the need to translate ethical principles into practice? What motivates AI practitioners to embed ethical principles into design practices? What barriers do AI practitioners face when attempting to translate ethical principles into practice? And finally, what assistance do AI practitioners want and need when translating ethical principles into practice? (shrink)

Automated vehicles (AVs) are expected to operate on public roads, together with non-automated vehicles and other road users such as pedestrians or bicycles. Recent ethical reports and guidelines raise worries that AVs will introduce injustice or reinforce existing social inequalities in road traffic. One major injustice concern in today’s traffic is that different types of road users are exposed differently to risks of corporal harm. In the first part of the paper, we discuss the responsibility of AV developers to address (...) existing injustice concerns regarding risk exposure as well as approaches on how to fulfill the responsibility for a fairer distribution of risk. In contrast to popular approaches on the ethics of risk distribution in unavoidable accident cases, we focus on low and moderate risk situations, referred to as routine driving. For routine driving, the obligation to distribute risks fairly must be discussed in the context of risk-taking and risk-acceptance, balancing safety objectives of occupants and other road users with driving utility. In the second part of the paper, we present a typical architecture for decentralized automated driving which contains a dedicated module for real-time risk estimation and management. We examine how risk estimation modules can be adjusted and parameterized to redress some inequalities. (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)

This paper delves into the use of machine learning-based systems in decision-making processes and its implications for moral responsibility as traditionally defined. It focuses on the emergence of responsibility gaps and examines proposed strategies to address them. The paper aims to provide an introductory and comprehensive overview of the ongoing debate surrounding moral responsibility in automated decision-making. By thoroughly examining these issues, we seek to contribute to a deeper understanding of the implications of AI integration in society.

Hype over novel quantum technologies has prompted discussion on the likely societal impacts of the sector. Calls to ensure the responsible development of quantum technologies are complicated by a lack of concrete case studies or real-world examples of irresponsible quantum. At this stage, responsible quantum faces a situation reminiscent of the Collingridge dilemma. In this dilemma, the moment in which discussion on societal risks and benefits can be most impactful is also the time when the least information is available. The (...) flipside of this challenge is an opportunity to build processes for examining the public good of quantum before the trajectory (and potential problems) of the sector become “locked in”. Recent work in this space has argued that quantum researchers and innovators must work with society to address uncertainties and concerns. By engaging quantum stakeholders and understanding their perspectives on responsibility, this paper seeks to support this proposition and enable further dialogue on responsible development and use of quantum technologies. (shrink)

This paper draws on the emerging field of innovation ethics to complement the more established field of responsible innovation by focusing on key ethical issues raised by technological innovations. One key limitation of influential frameworks of RI is that they tend to neglect some key ethical issues raised by innovation, as well as major normative dimensions of the notion of responsibility. We explain how IE could enrich RI by stressing the more important role that ethical analysis should play in RI. (...) We focus on two transversal issues of IE: the issue of redrawing conceptual boundaries, especially the topic of the artificialization of the world, and the issue of responsibility, especially the notion of total responsibility. We address these two issues from the thematic perspective of IE, thereby generating lessons learnt for RI. These two examples are taken as illustrations and blueprint of the dialogue that should take place between the two fields. (shrink)

Responsibility has become a central concept in AI ethics; however, little research has been conducted into practitioners’ personal understandings of responsibility in the context of AI, including how responsibility should be defined and who is responsible when something goes wrong. In this article, we present findings from a 2020–2021 data set of interviews with AI practitioners and tech workers at a single multinational technology company and interpret them through the lens of feminist political thought. We reimagine responsibility in the context (...) of AI development and deployment as the product of work cultures that enable tech workers to be responsive and answerable for their products over the long and short term. From our interviews, we identify three key pain points in understanding the distribution of responsibility between actors and developing responsible design and deployment practices: (1) unstable business ecosystems and AI lifecycles, which require an approach to responsibility that accounts for the dynamic nature of these systems; (2) the issue of incentivizing engineers to take responsibility for the mundane maintenance practices essential to the functioning of AI systems and (3) the need to overcome individual and structural barriers to taking ownership over AI products and their effects. From these findings, we make three recommendations based on feminist theory: (1) organisations should move from a static model of responsibility to a dynamic and ethically motivated response-ability; (2) companies need to revalue care and maintenance practices; and (3) firms must move away from individualistic ideas of responsibility towards fostering wider cultures of responsibility. (shrink)

Conceptual Engineering (CE) is thought to be generally aimed at ameliorating deficient concepts. In this paper, we challenge this assumption: we argue that CE is frequently undertaken with the orthogonal aim of _conceptual adaptation_. We develop this thesis with reference to the interplay between technology and concepts. Emerging technologies can exert significant pressure on conceptual systems and spark ‘conceptual disruption’. For example, advances in Artificial Intelligence raise the question of whether AIs are agents or mere objects, which can be construed (...) as a CE question regarding the concepts AGENT and OBJECT. We distinguish between three types of conceptual disruption (conceptual gaps, conceptual overlaps, and conceptual misalignments) and argue that when CE occurs to address these disruptions, its primary aim is not to improve concepts, but to retain their functional quality, or to prevent them from degrading. This is the characteristic aim of CE when undertaken in philosophy of technology: to _preserve_ the functional role of a concept or conceptual scheme, rather than _improving_ how a concept fulfills its respective function. (shrink)

As the multiple issues of the digital public sphere threaten our democracies and the cohesion of our societies, most attempts for a betterment of the digital networks and platforms revolve around a risk-response approach. This paper takes the opposite approach and develops a positive definition of the ideal ethical public sphere, combining normative features with original taxonomies. In view of defining common standards for a healthy digital public sphere, this paper offers an interdisciplinary literature review, and original recommendations, before discussing (...) potential leverages for implementing these changes. The suggested ethical normative features derive from a positive approach to the digital public sphere, as ideal common standards. Sustained by the underlying assumption that the composite landscape of the media ecosystem and its different layers (networks, platforms, users) cannot be steered by government regulations alone, the normative (deontological) approach serves to understand and justify the need for a normative approach, to help envision new pathways for an ethical transformation. (shrink)

The use of drones in public healthcare is suggested as a means to improve efficiency under constrained resources and personnel. This paper begins by framing drones in healthcare as a social experiment where ethical guidelines are needed to protect those impacted while fully realizing the benefits the technology offers. Then we propose an ethical framework to facilitate the design, development, implementation, and assessment of drones used in public healthcare. Given the healthcare context, we structure the framework according to the four (...) bioethics principles: beneficence, non-maleficence, autonomy, and justice, plus a fifth principle from artificial intelligence ethics: explicability. These principles are abstract which makes operationalization a challenge; therefore, we suggest an approach of translation according to a values hierarchy whereby the top-level ethical principles are translated into relevant human values within the domain. The resulting framework is an applied ethics tool that facilitates awareness of relevant ethical issues during the design, development, implementation, and assessment of drones in public healthcare. (shrink)

Enacting an AI system typically requires three iterative phases where AI engineers are in command: selection and preparation of the data, selection and configuration of algorithmic tools, and fine-tuning of the different parameters on the basis of intermediate results. Our main hypothesis is that these phases involve practices with ethical questions. This paper maps these ethical questions and proposes a way to address them in light of a neo-republican understanding of freedom, defined as absence of domination. We thereby identify different (...) types of responsibility held by AI engineers and link them to concrete suggestions on how to improve professional practices. This paper contributes to the literature on AI and ethics by focusing on the work necessary to configure AI systems, thereby offering an input to better practices and an input for societal debates. (shrink)