Traditionally, the manufacturer/operator of a machine is held (morally and legally) responsible for the consequences of its operation. Autonomous, learning machines, based on neural networks, genetic algorithms and agent architectures, create a new situation, where the manufacturer/operator of the machine is in principle not capable of predicting the future machine behaviour any more, and thus cannot be held morally responsible or liable for it. The society must decide between not using this kind of machine any more (which is not a (...) realistic option), or facing a responsibility gap, which cannot be bridged by traditional concepts of responsibility ascription. (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)

New technologies from artificial intelligence to drones, and biomedical enhancement make the future of the human family increasingly hard to predict and protect. This book explores how the philosophical tradition of virtue ethics can help us to cultivate the moral wisdom we need to live wisely and well with emerging technologies.

Since the 1970s Gary Watson has published a series of brilliant and highly influential essays on human action, examining such questions as: in what ways are we free and not free, rational and irrational, responsible or not for what we do? Moral philosophers and philosophers of action will welcome this collection, representing one of the most important bodies of work in the field.

One of the most noticeable trends in recent years has been the increasing reliance of public decision-making processes on algorithms, i.e. computer-programmed step-by-step instructions for taking a given set of inputs and producing an output. The question raised by this article is whether the rise of such algorithmic governance creates problems for the moral or political legitimacy of our public decision-making processes. Ignoring common concerns with data protection and privacy, it is argued that algorithmic governance does pose a significant threat (...) to the legitimacy of such processes. Modelling my argument on Estlund’s threat of epistocracy, I call this the ‘threat of algocracy’. The article clarifies the nature of this threat and addresses two possible solutions. It is argued that neither solution is likely to be successful, at least not without risking many other things we value about social decision-making. The result is a somewhat pessimistic conclusion in which we confront the possibility that we are creating decision-making processes that constrain and limit opportunities for human participation. (shrink)

This paper adds another argument to the rising tide of panic about robots and AI. The argument is intended to have broad civilization-level significance, but to involve less fanciful speculation about the likely future intelligence of machines than is common among many AI-doomsayers. The argument claims that the rise of the robots will create a crisis of moral patiency. That is to say, it will reduce the ability and willingness of humans to act in the world as responsible moral agents, (...) and thereby reduce them to moral patients. Since that ability and willingness is central to the value system in modern liberal democratic states, the crisis of moral patiency has a broad civilization-level significance: it threatens something that is foundational to and presupposed in much contemporary moral and political discourse. I defend this argument in three parts. I start with a brief analysis of an analogous argument made in pop culture. Though those arguments turn out to be hyperbolic and satirical, they do prove instructive as they illustrates a way in which the rise of robots could impact upon civilization, even when the robots themselves are neither malicious nor powerful enough to bring about our doom. I then introduce the argument from the crisis of moral patiency, defend its main premises and address objections. (shrink)

We are living through an era of increased robotisation. Some authors have already begun to explore the impact of this robotisation on legal rules and practice. In doing so, many highlight potential liability gaps that might arise through robot misbehaviour. Although these gaps are interesting and socially significant, they do not exhaust the possible gaps that might be created by increased robotisation. In this article, I make the case for one of those alternative gaps: the retribution gap. This gap arises (...) from a mismatch between the human desire for retribution and the absence of appropriate subjects of retributive blame. I argue for the potential existence of this gap in an era of increased robotisation; suggest that it is much harder to plug this gap than it is to plug those thus far explored in the literature; and then highlight three important social implications of this gap. (shrink)

Human obsolescence is imminent. We are living through an era in which our activity is becoming less and less relevant to our well-being and to the fate of our planet. This trend toward increased obsolescence is likely to continue in the future, and we must do our best to prepare ourselves and our societies for this reality. Far from being a cause for despair, this is in fact an opportunity for optimism. Harnessed in the right way, the technology that hastens (...) our obsolescence can open us up to new utopian possibilities and enable heightened forms of human flourishing. (shrink)

Can we build ‘moral robots’? If morality depends on emotions, the answer seems negative. Current robots do not meet standard necessary conditions for having emotions: they lack consciousness, mental states, and feelings. Moreover, it is not even clear how we might ever establish whether robots satisfy these conditions. Thus, at most, robots could be programmed to follow rules, but it would seem that such ‘psychopathic’ robots would be dangerous since they would lack full moral agency. However, I will argue that (...) in the future we might nevertheless be able to build quasi-moral robots that can learn to create the appearance of emotions and the appearance of being fully moral. I will also argue that this way of drawing robots into our social-moral world is less problematic than it might first seem, since human morality also relies on such appearances. (shrink)

This paper discusses the problem of responsibility attribution raised by the use of artificial intelligence technologies. It is assumed that only humans can be responsible agents; yet this alone already raises many issues, which are discussed starting from two Aristotelian conditions for responsibility. Next to the well-known problem of many hands, the issue of “many things” is identified and the temporal dimension is emphasized when it comes to the control condition. Special attention is given to the epistemic condition, which draws (...) attention to the issues of transparency and explainability. In contrast to standard discussions, however, it is then argued that this knowledge problem regarding agents of responsibility is linked to the other side of the responsibility relation: the addressees or “patients” of responsibility, who may demand reasons for actions and decisions made by using AI. Inspired by a relational approach, responsibility as answerability thus offers an important additional, if not primary, justification for explainability based, not on agency, but on patiency. (shrink)

The question of whether AI systems such as robots can or should be afforded moral agency or patiency is not one amenable either to discovery or simple reasoning, because we as societies constantly reconstruct our artefacts, including our ethical systems. Consequently, the place of AI systems in society is a matter of normative, not descriptive ethics. Here I start from a functionalist assumption, that ethics is the set of behaviour that maintains a society. This assumption allows me to exploit the (...) theoretical biology of sociality and autonomy to explain our moral intuitions. From this grounding I extend to consider possible ethics for maintaining either human- or of artefact-centred societies. I conclude that while constructing AI systems as either moral agents or patients is possible, neither is desirable. In particular, I argue that we are unlikely to construct a coherent ethics in which it it is ethical to afford AI moral subjectivity. We are therefore obliged not to build AI we are obliged to. (shrink)

This article gives an account of what makes achievements valuable. Although the natural thought is that achievements are valuable because of the product, such as a cure for cancer or a work of art, I argue that the value of the product of an achievement is not sufficient to account for its overall value. Rather, I argue that achievements are valuable in virtue of their difficulty. I propose a new perfectionist theory of value that acknowledges the will as a characteristic (...) human capacity, and thus holds that the exercise of the will, and therefore difficulty, is intrinsically valuable. (shrink)

This chapter discusses blameworthiness for problematic acts that an agent does inadvertently. Blameworthiness, as opposed to liability, is difficult to make sense of in this sort of case, as there is usually thought to be a tight connection between blameworthiness and something in the agent’s quality of will. This chapter argues that in personal relationships we should sometimes take responsibility for inadvertent actions. Taking on responsibility when we inadvertently fail in our duties to our loved ones assures them that we (...) respect them, take them seriously, and want to be respected and taken seriously in turn. The chapter ends with a defence of the claim that this is a genuine sort of blameworthiness. -/- . (shrink)

Selected by the Economist as one of the best books of 2020. -/- Privacy Is Power argues that people should protect their personal data because privacy is a kind of power. If we give too much of our data to corporations, the wealthy will rule. If we give too much personal data to governments, we risk sliding into authoritarianism. For democracy to be strong, the bulk of power needs to be with the citizenry, and whoever has the data will have (...) the power. Privacy, I argue, is not a personal preference; it is a political concern. I also argue that personal data is a toxic asset, and should be regulated as if it were a toxic substance, similar to asbestos. I call for a complete ban to the trade in personal data. The book is at once philosophical and political, and extremely practical and accessible. It discusses liberal democracy, equality, justice, and autonomy. It includes a chapter for policymakers, and one that addresses what ordinary citizens can do to protect privacy. Finally, the book covers the tension between privacy and public health in the context of covid19. -/- . (shrink)

This book argues that we need to explore how human beings can best coordinate and collaborate with robots in responsible ways. It investigates ethically important differences between human agency and robot agency to work towards an ethics of responsible human-robot interaction.

R. Jay Wallace argues in this book that moral accountability hinges on questions of fairness: When is it fair to hold people morally responsible for what they do? Would it be fair to do so even in a deterministic world? To answer these questions, we need to understand what we are doing when we hold people morally responsible, a stance that Wallace connects with a central class of moral sentiments, those of resentment, indignation, and guilt. To hold someone responsible, he (...) argues, is to be subject to these reactive emotions in one's dealings with that person. Developing this theme with unusual sophistication, he offers a new interpretation of the reactive emotions and traces their role in our practices of blame and moral sanction. With this account in place, Wallace advances a powerful and sustained argument against the common view that accountability requires freedom of will. Instead, he maintains, the fairness of holding people responsible depends on their rational competence: the power to grasp moral reasons and to control their behavior accordingly. He shows how these forms of rational competence are compatible with determinism. At the same time, giving serious consideration to incompatibilist concerns, Wallace develops a compelling diagnosis of the common assumption that freedom is necessary for responsibility. Rigorously argued, eminently readable, this book touches on issues of broad concern to philosophers, legal theorists, political scientists, and anyone with an interest in the nature and limits of responsibility. (shrink)

Many industry leaders and academics from the field of machine ethics would have us believe that the inevitability of robots coming to have a larger role in our lives demands that robots be endowed with moral reasoning capabilities. Robots endowed in this way may be referred to as artificial moral agents. Reasons often given for developing AMAs are: the prevention of harm, the necessity for public trust, the prevention of immoral use, such machines are better moral reasoners than humans, and (...) building these machines would lead to a better understanding of human morality. Although some scholars have challenged the very initiative to develop AMAs, what is currently missing from the debate is a closer examination of the reasons offered by machine ethicists to justify the development of AMAs. This closer examination is especially needed because of the amount of funding currently being allocated to the development of AMAs coupled with the amount of attention researchers and industry leaders receive in the media for their efforts in this direction. The stakes in this debate are high because moral robots would make demands on society; answers to a host of pending questions about what counts as an AMA and whether they are morally responsible for their behavior or not. This paper shifts the burden of proof back to the machine ethicists demanding that they give good reasons to build AMAs. The paper argues that until this is done, the development of commercially available AMAs should not proceed further. (shrink)

Many industry leaders and academics from the field of machine ethics would have us believe that the inevitability of robots coming to have a larger role in our lives demands that robots be endowed with moral reasoning capabilities. Robots endowed in this way may be referred to as artificial moral agents. Reasons often given for developing AMAs are: the prevention of harm, the necessity for public trust, the prevention of immoral use, such machines are better moral reasoners than humans, and (...) building these machines would lead to a better understanding of human morality. Although some scholars have challenged the very initiative to develop AMAs, what is currently missing from the debate is a closer examination of the reasons offered by machine ethicists to justify the development of AMAs. This closer examination is especially needed because of the amount of funding currently being allocated to the development of AMAs coupled with the amount of attention researchers and industry leaders receive in the media for their efforts in this direction. The stakes in this debate are high because moral robots would make demands on society; answers to a host of pending questions about what counts as an AMA and whether they are morally responsible for their behavior or not. This paper shifts the burden of proof back to the machine ethicists demanding that they give good reasons to build AMAs. The paper argues that until this is done, the development of commercially available AMAs should not proceed further. (shrink)

This paper explores the ambiguous impact of new information and communications technologies on the cultivation of moral skills in human beings. Just as twentieth century advances in machine automation resulted in the economic devaluation of practical knowledge and skillsets historically cultivated by machinists, artisans, and other highly trained workers , while also driving the cultivation of new skills in a variety of engineering and white collar occupations, ICTs are also recognized as potential causes of a complex pattern of economic deskilling, (...) reskilling, and upskilling. In this paper, I adapt the conceptual apparatus of sociological debates over economic deskilling to illuminate a different potential for technological deskilling/upskilling, namely the ability of ICTs to contribute to the moral deskilling of human users, a potential that exists alongside rich but currently underrealized possibilities for moral reskilling and/or upskilling. I flesh out this general hypothesis by means of examples involving automated weapons technology, new media practices, and social robotics. I conclude that since moral skills are essential prerequisites for the effective development of practical wisdom and virtuous character, and since market and cultural forces are not presently aligned to bring about the more salutary of the ambiguous potentials presented here, the future shape of these developments warrants our close attention—and perhaps active intervention. (shrink)

Medical errors are all too common. Ever since a report issued by the Institute of Medicine raised awareness of this unfortunate reality, an emerging theme has gained prominence in the literature on medical error. Fears of blame and punishment, it is often claimed, allow errors to remain undisclosed. Accordingly, modern healthcare must shift away from blame towards a culture of safety in order to effectively reduce the occurrence of error. Against this shift, I argue that it would serve the medical (...) community well to retain notions of individual responsibility and blame in healthcare settings. In particular, expressions of moral emotions—such as guilt, regret and remorse—appear to play an important role in the process of disclosing harmful errors to patients and families. While such self-blaming responses can have negative psychological effects on the individual practitioner, those who take the blame are in the best position to offer apologies and show that mistakes are being taken seriously, thereby allowing harmed patients and families to move forward in the wake of medical error. (shrink)

Robotic and artificially intelligent (AI) systems are becoming prevalent in our day-to-day lives. As human interaction is increasingly replaced by human–computer and human–robot interaction (HCI and HRI), we occasionally speak and act as though we are blaming or praising various technological devices. While such responses may arise naturally, they are still unusual. Indeed, for some authors, it is the programmers or users—and not the system itself—that we properly hold responsible in these cases. Furthermore, some argue that since directing blame or (...) praise at technology itself is unfitting, designing systems in ways that encourage such practices can only exacerbate the problem. On the other hand, there may be good moral reasons to continue engaging in our natural practices, even in cases involving AI systems or robots. In particular, daily interactions with technology may stand to impact the development of our moral practices in human-to-human interactions. In this paper, we put forward an empirically grounded argument in favor of some technologies being designed for social responsiveness. Although our usual practices will likely undergo adjustments in response to innovative technologies, some systems which we encounter can be designed to accommodate our natural moral responses. In short, fostering HCI and HRI that sustains and promotes our natural moral practices calls for a co-developmental process with some AI and robotic technologies. (shrink)

In a landmark essay, Andreas Matthias claimed that current developments in autonomous, artificially intelligent systems are creating a so-called responsibility gap, which is allegedly ever-widening and stands to undermine both the moral and legal frameworks of our society. But how severe is the threat posed by emerging technologies? In fact, a great number of authors have indicated that the fear is thoroughly instilled. The most pessimistic are calling for a drastic scaling-back or complete moratorium on AI systems, while the optimists (...) aim to show that the gap can be bridged nonetheless. Contrary to both camps, I argue against the prevailing assumption that there is a technology-based responsibility gap. I show how moral responsibility is a dynamic and flexible process, one that can effectively encompass emerging technological entities. (shrink)

The United States Army’s Future Combat Systems Project, which aims to manufacture a “robot army” to be ready for deployment by 2012, is only the latest and most dramatic example of military interest in the use of artificially intelligent systems in modern warfare. This paper considers the ethics of a decision to send artificially intelligent robots into war, by asking who we should hold responsible when an autonomous weapon system is involved in an atrocity of the sort that would normally (...) be described as a war crime. A number of possible loci of responsibility for robot war crimes are canvassed; the persons who designed or programmed the system, the commanding officer who ordered its use, the machine itself. I argue that in fact none of these are ultimately satisfactory. Yet it is a necessary condition for fighting a just war, under the principle of jus in bellum, that someone can be justly held responsible for deaths that occur in the course of the war. As this condition cannot be met in relation to deaths caused by an autonomous weapon system it would therefore be unethical to deploy such systems in warfare. (shrink)

There is widespread agreement that there should be a principle requiring that artificial intelligence be ‘explicable’. Microsoft, Google, the World Economic Forum, the draft AI ethics guidelines for the EU commission, etc. all include a principle for AI that falls under the umbrella of ‘explicability’. Roughly, the principle states that “for AI to promote and not constrain human autonomy, our ‘decision about who should decide’ must be informed by knowledge of how AI would act instead of us” :689–707, 2018). There (...) is a strong intuition that if an algorithm decides, for example, whether to give someone a loan, then that algorithm should be explicable. I argue here, however, that such a principle is misdirected. The property of requiring explicability should attach to a particular action or decision rather than the entity making that decision. It is the context and the potential harm resulting from decisions that drive the moral need for explicability—not the process by which decisions are reached. Related to this is the fact that AI is used for many low-risk purposes for which it would be unnecessary to require that it be explicable. A principle requiring explicability would prevent us from reaping the benefits of AI used in these situations. Finally, the explanations given by explicable AI are only fruitful if we already know which considerations are acceptable for the decision at hand. If we already have these considerations, then there is no need to use contemporary AI algorithms because standard automation would be available. In other words, a principle of explicability for AI makes the use of AI redundant. (shrink)

Recent rapid advances in Artificial Intelligence and Machine Learning have raised many questions about the regulatory and governance mechanisms for autonomous machines. Many commentators, scholars, and policy-makers now call for ensuring that algorithms governing our lives are transparent, fair, and accountable. Here, I propose a conceptual framework for the regulation of AI and algorithmic systems. I argue that we need tools to program, debug and maintain an algorithmic social contract, a pact between various human stakeholders, mediated by machines. To achieve (...) this, we can adapt the concept of human-in-the-loop from the fields of modeling and simulation, and interactive machine learning. In particular, I propose an agenda I call society-in-the-loop, which combines the HITL control paradigm with mechanisms for negotiating the values of various stakeholders affected by AI systems, and monitoring compliance with the agreement. In short, ‘SITL = HITL + Social Contract.’. (shrink)

We propose that the prevalent moral aversion to AWS is supported by a pair of compelling objections. First, we argue that even a sophisticated robot is not the kind of thing that is capable of replicating human moral judgment. This conclusion follows if human moral judgment is not codifiable, i.e., it cannot be captured by a list of rules. Moral judgment requires either the ability to engage in wide reflective equilibrium, the ability to perceive certain facts as moral considerations, moral (...) imagination, or the ability to have moral experiences with a particular phenomenological character. Robots cannot in principle possess these abilities, so robots cannot in principle replicate human moral judgment. If robots cannot in principle replicate human moral judgment then it is morally problematic to deploy AWS with that aim in mind. Second, we then argue that even if it is possible for a sufficiently sophisticated robot to make ‘moral decisions’ that are extensionally indistinguishable from (or better than) human moral decisions, these ‘decisions’ could not be made for the right reasons. This means that the ‘moral decisions’ made by AWS are bound to be morally deficient in at least one respect even if they are extensionally indistinguishable from human ones. Our objections to AWS support the prevalent aversion to the employment of AWS in war. They also enjoy several significant advantages over the most common objections to AWS in the literature. (shrink)

Many ethicists writing about automated systems attribute agency to these systems. Not only that; they seemingly attribute an autonomous or independent form of agency to these machines. This leads some ethicists to worry about responsibility-gaps and retribution-gaps in cases where automated systems harm or kill human beings. In this paper, I consider what sorts of agency it makes sense to attribute to most current forms of automated systems, in particular automated cars and military robots. I argue that whereas it indeed (...) makes sense to attribute different forms of fairly sophisticated agency to these machines, we ought not to regard them as acting on their own, independently of any human beings. Rather, the right way to understand the agency exercised by these machines is in terms of human–robot collaborations, where the humans involved initiate, supervise, and manage the agency of their robotic collaborators. This means, I argue, that there is much less room for justified worries about responsibility-gaps and retribution-gaps than many ethicists think. (shrink)

This essay warns of eroding accountability in computerized societies. It argues that assumptions about computing and features of situations in which computers are produced create barriers to accountability. Drawing on philosophical analyses of moral blame and responsibility, four barriers are identified: 1) the problem of many hands, 2) the problem of bugs, 3) blaming the computer, and 4) software ownership without liability. The paper concludes with ideas on how to reverse this trend.

Traditionally, the manufacturer/operator of a machine is held (morally and legally) responsible for the consequences of its operation. Autonomous, learning machines, based on neural networks, genetic algorithms and agent architectures, create a new situation, where the manufacturer/operator of the machine is in principle not capable of predicting the future machine behaviour any more, and thus cannot be held morally responsible or liable for it. The society must decide between not using this kind of machine any more (which is not a (...) realistic option), or facing a responsibility gap, which cannot be bridged by traditional concepts of responsibility ascription. (shrink)

The development of highly humanoid sex robots is on the technological horizon. If sex robots are integrated into the legal community as “electronic persons”, the issue of sexual consent arises, which is essential for legally and morally permissible sexual relations between human persons. This paper explores whether it is conceivable, possible, and desirable that humanoid robots should be designed such that they are capable of consenting to sex. We consider reasons for giving both “no” and “yes” answers to these three (...) questions by examining the concept of consent in general, as well as critiques of its adequacy in the domain of sexual ethics; the relationship between consent and free will; and the relationship between consent and consciousness. Additionally we canvass the most influential existing literature on the ethics of sex with robots. (shrink)

In this article, we develop the concept of Transparency by Design that serves as practical guidance in helping promote the beneficial functions of transparency while mitigating its challenges in automated-decision making environments. With the rise of artificial intelligence and the ability of AI systems to make automated and self-learned decisions, a call for transparency of how such systems reach decisions has echoed within academic and policy circles. The term transparency, however, relates to multiple concepts, fulfills many functions, and holds different (...) promises that struggle to be realized in concrete applications. Indeed, the complexity of transparency for ADM shows tension between transparency as a normative ideal and its translation to practical application. To address this tension, we first conduct a review of transparency, analyzing its challenges and limitations concerning automated decision-making practices. We then look at the lessons learned from the development of Privacy by Design, as a basis for developing the Transparency by Design principles. Finally, we propose a set of nine principles to cover relevant contextual, technical, informational, and stakeholder-sensitive considerations. Transparency by Design is a model that helps organizations design transparent AI systems, by integrating these principles in a step-by-step manner and as an ex-ante value, not as an afterthought. (shrink)

We argue that artificial networks are explainable and offer a novel theory of interpretability. Two sets of conceptual questions are prominent in theoretical engagements with artificial neural networks, especially in the context of medical artificial intelligence: Are networks explainable, and if so, what does it mean to explain the output of a network? And what does it mean for a network to be interpretable? We argue that accounts of “explanation” tailored specifically to neural networks have ineffectively reinvented the wheel. In (...) response to, we show how four familiar accounts of explanation apply to neural networks as they would to any scientific phenomenon. We diagnose the confusion about explaining neural networks within the machine learning literature as an equivocation on “explainability,” “understandability” and “interpretability.” To remedy this, we distinguish between these notions, and answer by offering a theory and typology of interpretation in machine learning. Interpretation is something one does to an explanation with the aim of producing another, more understandable, explanation. As with explanation, there are various concepts and methods involved in interpretation: Total or Partial, Global or Local, and Approximative or Isomorphic. Our account of “interpretability” is consistent with uses in the machine learning literature, in keeping with the philosophy of explanation and understanding, and pays special attention to medical artificial intelligence systems. (shrink)

In this book Michael McKenna advances a new theory of moral responsibility, one that builds upon the work of P. F. Strawson. As McKenna demonstrates, moral responsibility can be explained on analogy with a conversation. The relation between a morally responsible agent and those who hold her morally responsible is similar to the relation between a speaker and her audience. A responsible agent's actions are bearers of meaning--agent meaning--just as a speaker's utterances are bearers of speaker meaning. Agent meaning is (...) a function of the moral quality of the will with which the agent acts. Those who hold an agent morally responsible for what she does do so by responding to her as if in a conversation. By responding with certain morally reactive attitudes, such as resentment or indignation, they thereby communicate their regard for the meaning taken to be revealed in that agent's actions. It is then open for the agent held responsible to respond to those holding her responsible by offering an apology, a justification, an excuse, or some other response, thereby extending the evolving conversational exchange.The conversational theory of moral responsibility that McKenna develops here accepts two features of Strawson's theory: that moral responsibility is essentially interpersonal--so that being responsible must be understood by reference to the nature of holding responsible--and that the moral emotions are central to holding responsible. While upholding these two aspects of Strawson's theory, McKenna's theory rejects a further Strawsonian thesis, which is that holding morally responsible is more fundamental or basic than being morally responsible. On the conversational theory, the conditions for holding responsible are dependent on the nature of the agent who is responsible. So holding responsible cannot be more basic than being responsible. Nevertheless, the nature of the agent who is morally responsible is to be understood in terms of sensitivity to those who would make moral demands of her, thereby holding her responsible. Being responsible is therefore also dependent on holding responsible. Thus, neither being nor holding morally responsible is more basic than the other. They are mutually dependent. (shrink)

Do we know what we're doing, and why? Psychological research seems to suggest not: reflection and self-awareness are surprisingly uncommon and inaccurate. John M. Doris presents a new account of agency and responsibility, which reconciles our understanding of ourselves as moral agents with empirical work on the unconscious mind.

David Shoemaker presents a new pluralistic theory of responsibility, based on the idea of quality of will. His approach is motivated by our ambivalence to real-life cases of marginal agency, such as those caused by clinical depression, dementia, scrupulosity, psychopathy, autism, intellectual disability, and poor formative circumstances. Our ambivalent responses suggest that such agents are responsible in some ways but not others. Shoemaker develops a theory to account for our ambivalence, via close examination of several categories of pancultural emotional responsibility (...) responses and their appropriateness conditions. The result is three distinct types of responsibility, each with its own set of required capacities: attributability, answerability, and accountability. Attributability is about the having and expressing of various traits of character, and it is the target of a range of aretaic sentiments and emotional practices organized around disdain and admiration. Answerability is about one’s capacity to govern one’s actions and attitudes by one’s evaluative judgments about the worth of various practical reasons, and it is the target of a range of sentiments and emotional practices organized around regret and pride. Accountability is about one’s ability to regard others, both evaluatively and emotionally, and it is the target of a range of sentiments and emotional practices organized around anger and gratitude. In Part One of the book, this tripartite theory is developed and defended. In Part Two of the book, the tripartite theory’s predictions about specific marginal cases are tested, once certain empirical details about the nature of those agents have been filled in and discussed. (shrink)

In this book Michael McKenna advances a new theory of moral responsibility, one that builds upon the work of P.F. Strawson.

The doyen of living English philosophers, by these reflections, took hold of and changed the outlook of a good many other philosophers, if not quite enough. He did so, essentially, by assuming that talk of freedom and responsibility is talk not of facts or truths, in a certain sense, but of our attitudes. His more explicit concern was to look again at the question of whether determinism and freedom are consistent with one another -- by shifting attention to certain personal (...) rather than moral attitudes, first of all gratitude and resentment. In the end, he arrived at a kind of Compatibilist or, as he says, Optimist conclusion. That is no doubt a recommendation but not the largest recommendation of this splendidly rich piece of philosophy. (shrink)

Rapid advances in AI-based automation have led to a number of existential and economic concerns. In particular, as automating technologies develop enhanced competency they seem to threaten the values associated with meaningful work. In this article, we focus on one such value: the value of achievement. We argue that achievement is a key part of what makes work meaningful and that advances in AI and automation give rise to a number achievement gaps in the workplace. This could limit people’s ability (...) to participate in meaningful forms of work. Achievement gaps are interesting, in part, because they are the inverse of the (negative) responsibility gaps already widely discussed in the literature on AI ethics. Having described and explained the problem of achievement gaps, the article concludes by identifying four possible policy responses to the problem. (shrink)

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. (shrink)