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)

AI has numerous applications and in various fields, including the military domain. The increase in the degree of autonomy in some decision-making systems leads to discussions on the possible future use of lethal autonomous weapons systems (LAWS). A central issue in these discussions is the assignment of moral responsibility for some AI-based outcomes. Several authors claim that the high autonomous capability of such systems leads to a so-called “responsibility gap.” In recent years, there has been a surge in (...) philosophical literature around the concept of responsibility gaps and different solutions have been devised to close or bridge these gaps. In order to move forward in the research around LAWS and the problem of responsibility, it is important to increase our understanding of the different perspectives and discussions in this debate. This paper attempts to do so by disentangling the various arguments and providing a critical overview. After giving a brief outline of the state of the technology of LAWS, I will review the debates over responsibility gaps using three differentiators: those who believe in the existence of responsibility gaps versus those who do not, those who hold that responsibility gaps constitute a new moral problem versus those who argue they do not, and those who claim that solutions can be successful as opposed to those who believe that it is an unsolvable problem. (shrink)

Who is responsible for a harm caused by AI, or a machine or system that relies on artificial intelligence? Given that current AI is neither conscious nor sentient, it’s unclear that AI itself is responsible for it. But given that AI acts independently of its developer or user, it’s also unclear that the developer or user is responsible for the harm. This gives rise to the so-called responsibility gap: cases where AI causes a harm, but no one is responsible (...) for it. Two central questions in the literature are whether responsibility gaps exist, and if yes, whether they’re morally problematic in a way that counts against developing or using AI. While some authors argue that responsibility gaps exist, and they’re morally problematic, some argue that they don’t exist. In this paper, I defend a novel position. First, I argue that current AI doesn’t generate a new kind of concern about responsibility that the older technologies don’t. Then, I argue that responsibility gaps exist but they’re unproblematic. (NOTE: Email me for a copy.). (shrink)

Responsibility gaps traditionally refer to scenarios in which no one is responsible for harm caused by artificial agents, such as autonomous machines or collective agents. By carefully examining the different ways this concept has been defined in the social ontology and ethics of technology literature, I argue that our current concept of responsibility gaps is defective. To address this conceptual flaw, I argue that the concept of responsibility gaps should be revised by distinguishing it into two more (...) precise concepts: epistemic responsibility gaps (ERG) and control misalignments (CM). ERG functions as a refined version addressing specific aspects of responsibility attribution. It captures the challenge of identifying who is to blame. CM, on the other hand, introduces a new dimension by focusing on misalignments of control. These revisions offer better guidelines for the development and oversight of artificial agents, both in terms of assigning blame and ensuring the right distribution of control. (shrink)

Artificial Intelligence (AI) systems are ubiquitous. From social media timelines, video recommendations on YouTube, and the kinds of adverts we see online, AI, in a very real sense, filters the world we see. More than that, AI is being embedded in agent-like systems, which might prompt certain reactions from users. Specifically, we might find ourselves feeling frustrated if these systems do not meet our expectations. In normal situations, this might be fine, but with the ever increasing sophistication of AI-systems, this (...) might become a problem. While it seems unproblematic to realize that being angry at your car for breaking down is unfitting, can the same be said for AI-systems? In this paper, therefore, I will investigate the so-called “reactive attitudes”, and their important link to our responsibility practices. I then show how within this framework there exist exemption and excuse conditions, and test whether our adopting the “objective attitude” toward agential AI is justified. I argue that such an attitude is appropriate in the context of three distinct senses of responsibility (answerability, attributability, and accountability), and that, therefore, AI-systems do not undermine our responsibility ascriptions. (shrink)

As sophisticated artificial intelligence software becomes more ubiquitously and more intimately integrated within domains of traditionally human endeavor, many are raising questions over how responsibility (be it moral, legal, or causal) can be understood for an AI’s actions or influence on an outcome. So called “responsibility gaps” occur whenever there exists an apparent chasm in the ordinary attribution of moral blame or responsibility when an AI automates physical or cognitive labor otherwise performed by human beings and commits (...) an error. Healthcare administration is an industry ripe for responsibility gaps produced by these kinds of AI. The moral stakes of healthcare are often life and death, and the demand for reducing clinical uncertainty while standardizing care incentivizes the development and integration of AI diagnosticians and prognosticators. In this paper, we argue that (1) responsibility gaps are generated by “black box” healthcare AI, (2) the presence of responsibility gaps (if unaddressed) creates serious moral problems, (3) a suitable solution is for relevant stakeholders to voluntarily responsibilize the gaps, taking on some moral responsibility for things they are not, strictly speaking, blameworthy for, and (4) should this solution be taken, black box healthcare AI will be permissible in the provision of healthcare. (shrink)

Which kinds of responsibility can we attribute to which kinds of collective, and why? In contrast, which kinds of collective responsibility can we not attribute—which kinds are ‘gappy’? This study provides a framework for answering these questions. It begins by distinguishing between three kinds of collective and three kinds of responsibility. It then explains how gaps—i.e. cases where we cannot attribute the responsibility we might want to—appear to arise within each type of collective responsibility. It (...) argues some of these gaps do not exist on closer inspection, at least for some collectives and some of the time. (shrink)

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)

Responsibility gaps arise when there is a mismatch between the amount of responsibility that can be attributed to any person or collection of persons on leading accounts of moral responsibility and the amount that robust intuitions suggest should be allocated to someone in a case. Claimed responsibility gaps arise in numerous philosophical debates, including those concerning government, corporate, and other forms of group agency and new technologies and those concerning theoretical issues in the philosophy of (...) class='Hi'>responsibility. This work is an opinionated introduction to and overview of recent work on responsibility gaps. It outlines and evaluates paradigmatic responsibility gap cases and ways of understanding the phenomenon as well as the existence conditions and moral status of and possible responses to responsibility gaps. It thereby contributes to ongoing work in the philosophy of responsibility and several applied domains. (shrink)

Recent work in philosophy of technology has come to bear on the question of responsibility gaps. Some authors argue that the increase in the autonomous capabilities of decision-making systems makes it impossible to properly attribute responsibility for AI-based outcomes. In this article we argue that one important, and often neglected, feature of recent debates on responsibility gaps is how this debate maps on to old debates in responsibility theory. More specifically, we suggest that one of the (...) key questions that is still at issue is the significance of the reactive attitudes, and how these ought to feature in our theorizing about responsibility. We will therefore provide a new descriptive categorization of different perspectives with respect to responsibility gaps. Such reflection can provide analytical clarity about what is at stake between the various interlocutors in this debate. The main upshot of our account is the articulation of a way to frame this ‘new’ debate by drawing on the rich intellectual history of ‘old’ concepts. By regarding the question of responsibility gaps as being concerned with questions of metaphysical priority, we see that the problem of these gaps lies not in any advanced technology, but rather in how we think about responsibility. (shrink)

In a landmark essay, Andreas Matthias claimed that current developments in autonomous, artificially intelligent (AI) 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)

Recent decades have witnessed tremendous progress in artificial intelligence and in the development of autonomous systems that rely on artificial intelligence. Critics, however, have pointed to the difficulty of allocating responsibility for the actions of an autonomous system, especially when the autonomous system causes harm or damage. The highly autonomous behavior of such systems, for which neither the programmer, the manufacturer, nor the operator seems to be responsible, has been suspected to generate responsibility gaps. This has been the (...) cause of much concern. In this article, I propose a more optimistic view on artificial intelligence, raising two challenges for responsibility gap pessimists. First, proponents of responsibility gaps must say more about when responsibility gaps occur. Once we accept a difficult-to-reject plausibility constraint on the emergence of such gaps, it becomes apparent that the situations in which responsibility gaps occur are unclear. Second, assuming that responsibility gaps occur, more must be said about why we should be concerned about such gaps in the first place. I proceed by defusing what I take to be the two most important concerns about responsibility gaps, one relating to the consequences of responsibility gaps and the other relating to violations of jus in bello. (shrink)

The article shows where the argument of responsibility-gap regarding brain-computer interfaces acquires its plausibility from, and suggests why the argument is not plausible. As a way of an explanation, a distinction between the descriptive third-person perspective and the interpretative first-person perspective is introduced. Several examples and metaphors are used to show that ascription of agency and responsibility does not, even in simple cases, require that people be in causal control of every individual detail involved in an event. Taking (...) up the current debate on liability in BCI use, the article provides and discusses some rules that should be followed when potentially harmful BCI-based devices are brought from the laboratory into everyday life. (shrink)

Sparrow argues that military robots capable of making their own decisions would be independent enough to allow us denial for their actions, yet too unlike us to be the targets of meaningful blame or praise—thereby fostering what Matthias has dubbed “the responsibility gap.” We agree with Sparrow that someone must be held responsible for all actions taken in a military conflict. That said, we think Sparrow overlooks the possibility of what we term “blank check” responsibility: A person of (...) sufficiently high standing could accept responsibility for the actions of autonomous robotic devices—even if that person could not be causally linked to those actions besides this prior agreement. The basic intuition behind our proposal is that humans can impute relations even when no other form of contact can be established. The missed alternative we want to highlight, then, would consist in an exchange: Social prestige in the occupation of a given office would come at the price of signing away part of one's freedoms to a contingent and unpredictable future guided by another agency. (shrink)

Due to its enormous potential, artificial intelligence (AI) can transform healthcare on a seemingly infinite scale. However, as we continue to explore the immense potential of AI, it is vital to consider the ethical concerns associated with its development and deployment. One specific concern that has been flagged in the literature is the responsibility gap (RG) due to the introduction of AI in healthcare. When the use of an AI algorithm or system results in a negative outcome for a (...) patient(s), to whom can or should responsibility for that outcome be assigned? Although the concept of the RG was introduced in Anglo-American and European philosophy, this paper aims to broaden the debate by providing an Ubuntu-inspired perspective on the RG. Ubuntu, deeply rooted in African philosophy, calls for collective responsibility, and offers a uniquely forward-looking approach to address the alleged RG caused by AI in healthcare. An Ubuntu-inspired perspective can serve as a valuable guide and tool when addressing the alleged RG. Incorporating Ubuntu into the AI ethics discourse can contribute to a more ethical and responsible integration of AI in healthcare. (shrink)

Many seem to think that AI-induced responsibility gaps are morally bad and therefore ought to be avoided. We argue, by contrast, that there is at least a pro tanto reason to welcome responsibility gaps. The central reason is that it can be bad for people to be responsible for wrongdoing. This, we argue, gives us one reason to prefer automated decision-making over human decision-making, especially in contexts where the risks of wrongdoing are high. While we are not the (...) first to suggest that responsibility gaps should sometimes be welcomed, our argument is novel. Others have argued that responsibility gaps should sometimes be welcomed because they can reduce or eliminate the psychological burdens caused by tragic moral choice-situations. By contrast, our argument explains why responsibility gaps should sometimes be welcomed even in the absence of tragic moral choice-situations, and even in the absence of psychological burdens. (shrink)

Danaher (2016) has argued that increasing robotization can lead to retribution gaps: Situations in which the normative fact that nobody can be justly held responsible for a harmful outcome stands in conflict with our retributivist moral dispositions. In this paper, we report a cross-cultural empirical study based on Sparrow’s (2007) famous example of an autonomous weapon system committing a war crime, which was conducted with participants from the US, Japan and Germany. We find that (1) people manifest a considerable willingness (...) to hold autonomous systems morally responsible, (2) partially exculpate human agents when interacting with such systems, and that more generally (3) the possibility of normative responsibility gaps is indeed at odds with people’s pronounced retributivist inclinations. We discuss what these results mean for potential implications of the retribution gap and other positions in the responsibility gap literature. (shrink)

In many cases of criminality within large corporations, senior management does not commit the operative offense—or conspire or assist in it—but nonetheless bears serious responsibility for the crime. That responsibility can derive from, among other things, management’s role in cultivating corporate culture, in failing to police effectively within the firm, and in accepting lavish compensation for taking the firm’s reins. Criminal law does not include any doctrinal means for transposing that form of responsibility into punishment. Arguments for (...) expanding doctrine—including broadening of the presently narrow “responsible corporate officer” doctrine—so as to authorize such punishment do not fare well under the justificatory demands of criminal law theory. The principal obstacle to such arguments is the large industrial corporation itself, which necessarily entails kinds and degrees of delegation and risk-taking that do not fit well with settled concepts about mens rea and omission liability. Even the most egregious and harmful management failures must be addressed through design and regulation of the corporation rather than imposition of individual criminal liability. (shrink)

A classic objection to autonomous weapon systems (AWS) is that these could create so-called responsibility gaps, where it is unclear who should be held responsible in the event that an AWS were to violate some portion of the law of armed conflict (LOAC). However, those who raise this objection generally do so presenting it as a problem for AWS as a whole class of weapons. Yet there exists a rather wide range of systems that can be counted as “autonomous (...) weapon systems”, and so the objection is too broad. In this article I present a taxonomic approach to the objection, examining a number of systems that would count as AWS under the prevalent definitions provided by the United States Department of Defense and the International Committee of the Red Cross, and I show that for virtually all such systems there is a clear locus of responsibility which presents itself as soon as one focuses on specific systems, rather than general notions of AWS. In developing these points, I also suggest a method for dealing with near-future types of AWS which may be thought to create situations where responsibility gaps can still arise. The main purpose of the arguments is, however, not to show that responsibility gaps do not exist or can be closed where they do exist. Rather, it is to highlight that any arguments surrounding AWS must be made with reference to specific weapon platforms imbued with specific abilities, subject to specific limitations, and deployed to specific times and places for specific purposes. More succinctly, the arguments show that we cannot and should not aim to treat AWS as if all of these shared all morally relevant features, but instead on a case-by-case basis. Thus, we must contend with the realities of weapons development and deployment, and tailor our arguments and conclusions to those realities, and with an eye to what facts obtain for particular systems fulfilling particular combat roles. (shrink)

When a computer system causes harm, who is responsible? This question has renewed significance given the proliferation of autonomous systems enabled by modern artificial intelligence techniques. At the root of this problem is a philosophical difficulty known in the literature as the responsibility gap. That is to say, because of the causal distance between the designers of autonomous systems and the eventual outcomes of those systems, the dilution of agency within the large and complex teams that design autonomous systems, (...) and the impossibility of fully predicting how autonomous systems will behave once deployed, determining who is morally responsible for harms caused by autonomous systems is unclear at a conceptual level. I review past work on this topic, criticizing prior works for suggesting workarounds rather than philosophical answers to the conceptual problem presented by the responsibility gap. The view I develop, drawing on my earlier work on vicarious moral responsibility, explains why computing professionals are ethically required to take responsibility for the systems they design, despite not being blameworthy for the harms these systems may cause. (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)

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.

AI has numerous applications and in various fields, including the military domain. The increase in the degree of autonomy in some decision-making systems leads to discussions on the possible future use of lethal autonomous weapons systems (LAWS). A central issue in these discussions is the assignment of moral responsibility for some AI-based outcomes. Several authors claim that the high autonomous capability of such systems leads to a so-called “responsibility gap.” In recent years, there has been a surge in (...) philosophical literature around the concept of responsibility gaps and different solutions have been devised to close or bridge these gaps. In order to move forward in the research around LAWS and the problem of responsibility, it is important to increase our understanding of the different perspectives and discussions in this debate. This paper attempts to do so by disentangling the various arguments and providing a critical overview. After giving a brief outline of the state of the technology of LAWS, I will review the debates over responsibility gaps using three differentiators: those who believe in the existence of responsibility gaps versus those who do not, those who hold that responsibility gaps constitute a new moral problem versus those who argue they do not, and those who claim that solutions can be successful as opposed to those who believe that it is an unsolvable problem. (shrink)

Responsibility gaps concern the attribution of blame for harms caused by autonomous machines. The worry has been that, because they are artificial agents, it is impossible to attribute blame, even though doing so would be appropriate given the harms they cause. We argue that there are no responsibility gaps. The harms can be blameless. And if they are not, the blame that is appropriate is indirect and can be attributed to designers, engineers, software developers, manufacturers or regulators. The (...) real problem lies elsewhere: autonomous machines should be built so as to exhibit a level of risk that is morally acceptable. If they fall short of this standard, they exhibit what we call ‘a control gap.’ The causal control that autonomous machines have will then fall short of the guidance control they should emulate. (shrink)

Artificial intelligence increasingly executes tasks that previously only humans could do, such as drive a car, fight in war, or perform a medical operation. However, as the very best AI systems tend to be the least controllable and the least transparent, some scholars argued that humans can no longer be morally responsible for some of the AI-caused outcomes, which would then result in a responsibility gap. In this paper, I assume, for the sake of argument, that at least some (...) of the most sophisticated AI systems do indeed create responsibility gaps, and I ask whether we can bridge these gaps at will, viz. whether certain people could take responsibility for AI-caused harm simply by performing a certain speech act, just as people can give permission for something simply by performing the act of consent. So understood, taking responsibility would be a genuine normative power. I first discuss and reject the view of Champagne and Tonkens, who advocate a view of taking liability. According to this view, a military commander can and must, ahead of time, accept liability to blame and punishment for any harm caused by autonomous weapon systems under her command. I then defend my own proposal of taking answerability, viz. the view that people can makes themselves morally answerable for the harm caused by AI systems, not only ahead of time but also when harm has already been caused. (shrink)

The possible future use of lethal autonomous weapons systems (LAWS) and the challenges associated with assigning moral responsibility leads to several debates. Some authors argue that the highly autonomous capability of such systems may lead to a so-called responsibility gap in situations where LAWS cause serious violations of international humanitarian law. One proposed solution is the doctrine of command responsibility. Despite the doctrine’s original development to govern human interactions on the battlefield, it is worth considering whether the (...) doctrine of command responsibility could provide a solution by applying the notion analogously to LAWS. A fundamental condition underpinning the doctrine’s application is the control requirement, stipulating that a superior must exert some degree of control over subordinates. The aim of this article is to provide an in-depth analysis of this control condition and assess whether it leads to the impossibility of applying the doctrine of command responsibility to LAWS. To this end, the first section briefly introduces the topic of LAWS and responsibility gaps. The subsequent section provides a concise overview of the doctrine itself and the conditions typically necessitated for its application. In the third section, a comprehensive scrutiny of the control requirement is undertaken through examination of key case law, examining how the concept has been interpreted. Finally, the fourth section delves into the evaluation of commanders’ potential to exert effective control over their (non-human) subordinates. Based on this, the feasibility of considering command responsibility as a viable solution is assessed, aiming to determine whether its application should be prima facie excluded or warrants further exploration. (shrink)

Ethics settings allow for morally significant decisions made by humans to be programmed into autonomous machines, such as autonomous vehicles or autonomous weapons. Customizable ethics settings are a type of ethics setting in which the users of autonomous machines make such decisions. Here two arguments are provided in defence of customizable ethics settings. Firstly, by approaching ethics settings in the context of failure management, it is argued that customizable ethics settings are instrumentally and inherently valuable for building resilience into the (...) larger socio-technical systems in which autonomous machines operate. Secondly, after defining the preliminary condition of responsibility attribution and demonstrating how ethics settings enable humans to exert control over the outcomes of morally significant incidents, it is shown that ethics settings narrow the responsibility gap. (shrink)

Peter Königs recently argued that, while autonomous robots generate responsibility gaps, such gaps need not be considered problematic. I argue that Königs’ compromise dissolves under analysis since, on a proper understanding of what “responsibility” is and what “gap” (metaphorically) means, their joint endorsement must repel an attitude of indifference. So, just as “calamities that happen but don’t bother anyone” makes no sense, the idea of “responsibility gaps that exist but leave citizens and ethicists unmoved” makes no sense.

Artificial agents create significant moral opportunities and challenges. Over the last two decades, discourse has largely focused on the concept of a ‘responsibility gap.’ We argue that this concept is incoherent, misguided, and diverts attention from the core issue of ‘control gaps.’ Control gaps arise when there is a discrepancy between the causal control an agent exercises and the moral control it should possess or emulate. Such gaps present moral risks, often leading to harm or ethical violations. We propose (...) a second-order ‘duty of moral control’ that mandates closing these gaps to reduce risks within acceptable moral limits. Our analysis encompasses both autonomous machines and collective agents, acknowledging their similarities and key differences in constitution and moral status. We suggest four methods to close control gaps: ensuring artificial agents attain moral agency, providing meaningful human control, implementing safety engineering, and employing social control. These methods aim to responsibly integrate artificial agents into society. We conclude that a realistic approach, which addresses the practical problems posed by control gaps, is essential. This approach provides solutions to manage the risks posed by artificial agents while maintaining acceptable moral standards, ensuring we responsibly harness their potential and address the ethical challenges they present. (shrink)

이 논문은 자율적인 AI 로봇이 야기하는 책임 공백의 물음에 관한 철학적 논의를 통해 윤리학적 해결책으로 공동 책임론을 옹호하는 데 그 목적이 있다. 이를 위해 A. 매티어스가 제기한 책임 공백이 무엇인지를 해명한 다음 이에 관한 세 접근법, 즉, 인간 중심주의 접근법(도구주의 2.0), 기술중심주의 접근법(기계 윤리), 그리고 구성주의 접근법(하이브리드 책임)을 비판적으로 천착할 것이다. 이러한 비판적 논의는 책임 공백 물음의 해결에 행위성 개념이 중요한 역할을 차지하고 있음을 보여줄 것이다. ‘확장된 행위성 이론’에 근거하여 필자는 자율적인 AI 로봇 역시 도덕적 행위성을 지니기에 도덕적 책임의 주체가 (...) 될 수 있음을 역설할 것이다. 하지만 로봇은 오직 인간 행위자와 협업 관계를 맺는 경우에만 도덕적 행위성을 갖기에, 책임 공백의 물음은 하이브리드 접근법에 따라 인간과 기계의 공동 책임 내지 분산된 책임에 의해 그 해결책이 강구되어야 할 것이다. 그럼에도 발생할 수 있는 책임 공백의 문제는 인간 행위자에게 덕목으로서의 책임을 요청한다는 점을 맺는말에서 필자는 제안하고자 한다. (shrink)

In this paper, I will argue that the responsibility gap arising from new AI systems is reducible to the problem of many hands and collective agency. Systematic analysis of the agential dimension of AI will lead me to outline a disjunctive between the two problems. Either we reduce individual responsibility gaps to the many hands, or we abandon the individual dimension and accept the possibility of responsible collective agencies. Depending on which conception of AI agency we begin with, (...) the responsibility gap will boil down to one of these two moral problems. Moreover, I will adduce that this conclusion reveals an underlying weakness in AI ethics: the lack of attention to the question of the disciplinary boundaries of AI ethics. This absence has made it difficult to identify the specifics of the responsibility gap arising from new AI systems as compared to the responsibility gaps of other applied ethics. Lastly, I will be concerned with outlining these specific aspects. (shrink)

The responsibility gap, commonly described as a core challenge for the effective governance of, and trust in, AI and autonomous systems (AI/AS), is traditionally associated with a failure of the epistemic and/or the control condition of moral responsibility: the ability to know what we are doing and exercise competent control over this doing. Yet these two conditions are a red herring when it comes to understanding the responsibility challenges presented by AI/AS, since evidence from the cognitive sciences (...) shows that individual humans face very similar responsibility challenges with regard to these two conditions. While the problems of epistemic opacity and attenuated behaviour control are not unique to AI/AS technologies (though they can be exacerbated by them), we show that we can learn important lessons for AI/AS development and governance from how philosophers have recently revised the traditional concept of moral responsibility in response to these challenges to responsible human agency from the cognitive sciences. The resulting instrumentalist views of responsibility, which emphasize the forward-looking and flexible role of agency cultivation, hold considerable promise for integrating AI/AS into a healthy moral ecology. We note that there nevertheless is a gap in AI/AS responsibility that has yet to be extensively studied and addressed, one grounded in a relational asymmetry of vulnerability between human agents and sociotechnical systems like AI/AS. In the conclusion of this paper we note that attention to this vulnerability gap must inform and enable future attempts to construct trustworthy AI/AS systems and preserve the conditions for responsible human agency. (shrink)

According to David Miller, duties of domestic national and global justice are of equal importance, given that nationhood is both intrinsically valuable and not inherently an unjust way of excluding outsiders. The consequence of this?split?level? view is that it may be reasonable to prioritize domestic justice in some cases, while letting demands of global justice take precedence in others, depending on a weighting model which seeks to account for the relative urgency of domestic and global claims and the extent to (...) which agents are more closely attached to compatriots than to outsiders. In this chapter, I argue against this weighting model on grounds of internal coherence with the theory set out in National responsibility and global justice. I first inquire into the conditions under which justice at home conflicts with justice in the world at large, according to Miller?s main principle of global justice? respect for basic human rights. I then show that on Miller?s own understanding of the various duties of global and domestic justice, cases of conflict rarely arise, and that when they do, there is a powerful argument for prioritizing global duties on grounds of urgency, which contradicts the reasoning of the weighting model. Finally, I address a problem arising from Miller?s basic assumption that under?fulfillment of basic human rights in poor countries only generates claims of global justice to which rich nations are under a duty to respond, following the acceptance of a scheme of remedial responsibilities to provide aid by each of these nations. The normative structure of the split?level view set out in NRGJ needs to be clarified with respect to the key question of whether nations can be held to be under a duty of justice to bring such a scheme into existence in the first place. (shrink)

The purpose of this study is to examine whether corporate social responsibility activities perceived by consumers affect brand trust and corporate sustainability management. In other words, this study tried to examine whether the compatibility and authenticity of CSR influences brand trust, thereby affecting CSM including economic viability, environmental soundness, and social responsibility. To measure this, an empirical analysis was conducted on 479 consumers who had experience purchasing products from cosmetic companies that are carrying out CSR. As a result (...) of the analysis, it was found that the compatibility and authenticity of CSR have a positive effect on brand trust. Also, it was found that brand trust had a positive effect on social responsibility among the sub-concepts of CSM, but did not affect economic viability and environmental soundness. The results of this study are expected to provide strategic implications for social responsibility performance and brand trust building necessary for cosmetics companies to grow continuously. (shrink)

This study interrogates the Gap campaign from a political economic perspective to determine whether it goes beyond merely touting the virtuous line of social responsibility. Critics cite the irony of capitalist-based solutions that perpetuate the inequities they are trying to address. Others suggest the aid generated is problematic in and of itself because it keeps Africa from becoming self-sufficient. This research contends the purpose of the Gap’s participation is genuine, going beyond window dressing and the surface level benefit of (...) capitalistic enterprise, and is in fact linked to a higher motive of propagating ethical trade which ultimately offers African beneficiaries a path to financial independence. However, because the message of the current Gap advertising campaign does convey a false sense of doing good, another advertising approach is warranted. (shrink)

We argue that explainable artificial intelligence (XAI), specifically reason-giving XAI, often constitutes the most suitable way of ensuring that someone can properly be held responsible for decisions that are based on the outputs of artificial intelligent (AI) systems. We first show that, to close moral responsibility gaps (Matthias 2004), often a human in the loop is needed who is directly responsible for particular AI-supported decisions. Second, we appeal to the epistemic condition on moral responsibility to argue that, in (...) order to be responsible for her decision, the human in the loop has to have an explanation available of the system’s recommendation. Reason explanations are especially well-suited to this end and we examine whether – and how – it might be possible to make such explanations fit with AI systems. We support our claims by focusing on a case of disagreement between human in the loop and AI system. (shrink)

Automated technologies and robots make decisions that cannot always be fully controlled or predicted. In addition to that, they cannot respond to punishment and blame in the ways humans do. Therefore, when automated cars harm or kill people, for example, this gives rise to concerns about responsibility-gaps and retribution-gaps. According to Sven Nyholm, however, automated cars do not pose a challenge on human responsibility, as long as humans can control them and update them. He argues that the agency (...) exercised in automated cars should be understood in terms of human–robot collaborations. This brief note focuses on the problem that arises when there are multiple people involved, but there is no obvious shared collaboration among them. Building on John Danaher’s discussion of command responsibility, it is argued that, although Nyholm might be right that autonomous cars cannot be regarded as acting on their own, independently of any human beings, worries about responsibility-gaps and retribution-gaps are still justified, because it often remains unclear how to allocate or distribute responsibility satisfactorily among the key humans involved after they have been successfully identified. (shrink)

Are groups ever capable of bearing responsibility, over and above their individual members? This chapter discusses and defends the view that certain organized collectives – namely, those that qualify as group moral agents – can be held responsible for their actions, and that group responsibility is not reducible to individual responsibility. The view has important implications. It supports the recognition of corporate civil and even criminal liability in our legal systems, and it suggests that, by recognizing group (...) agents as loci of responsibility, we may be able to avoid “responsibility gaps” in some cases of collectively caused harms for which there is a shortfall of individual responsibility. The chapter further asks whether the view that certain groups are responsible agents commits us to the view that those groups should also be given rights of their own and gives a qualified negative answer. (shrink)

I argue for responsibility internalism. That is, moral responsibility (i.e., accountability, or being apt for praise or blame) depends only on factors internal to agents. Employing this view, I also argue that no one is responsible for what AI does but this isn’t morally problematic in a way that counts against developing or using AI. Responsibility is grounded in three potential conditions: the control (or freedom) condition, the epistemic (or awareness) condition, and the causal responsibility condition (...) (or consequences). I argue that causal responsibility is irrelevant for moral responsibility, and that the control condition and the epistemic condition depend only on factors internal to agents. Moreover, since what AI does is at best a consequence of our actions, and the consequences of our actions are irrelevant to our responsibility, no one is responsible for what AI does. That is, the so-called responsibility gap exits. However, this isn’t morally worrisome for developing or using AI. Firstly, I argue, current AI doesn’t generate a new kind of concern about responsibility that the older technologies don’t. Then, I argue that responsibility gap is not worrisome because neither responsibility gap, nor my argument for its existence, entails that no one can be justly punished, held accountable, or incurs duties in reparations when AI causes a harm. (shrink)

The task of this essay is to respond to the question concerning robots and responsibility—to answer for the way that we understand, debate, and decide who or what is able to answer for decisions and actions undertaken by increasingly interactive, autonomous, and sociable mechanisms. The analysis proceeds through three steps or movements. It begins by critically examining the instrumental theory of technology, which determines the way one typically deals with and responds to the question of responsibility when it (...) involves technology. It then considers three instances where recent innovations in robotics challenge this standard operating procedure by opening gaps in the usual way of assigning responsibility. The innovations considered in this section include: autonomous technology, machine learning, and social robots. The essay concludes by evaluating the three different responses—instrumentalism 2.0, machine ethics, and hybrid responsibility—that have been made in face of these difficulties in an effort to map out the opportunities and challenges of and for responsible robotics. (shrink)

The best-performing AI systems, such as deep neural networks, tend to be the ones that are most difficult to control and understand. For this reason, scholars worry that the use of AI would lead to so-called responsibility gaps, that is, situations in which no one is morally responsible for the harm caused by AI, because no one satisfies the so-called control condition and epistemic condition of moral responsibility. In this article, I acknowledge that there is a significant challenge (...) around responsibility and AI. Yet I don't think that this challenge is best captured in terms of a responsibility gap. Instead, I argue for the opposite view, namely that there is responsibility abundance, that is, a situation in which numerous agents are responsible for the harm caused by AI, and that the challenge comes from the difficulties of dealing with such abundance in practice. I conclude by arguing that reframing the challenge in this way offers distinct dialectic and theoretical advantages, promising to help overcome some obstacles in the current debate surrounding ‘responsibility gaps’. (shrink)

Future weapons will make life-or-death decisions without a human in the loop. When such weapons inflict unwarranted harm, no one appears to be responsible. There seems to be a responsibility gap. I first reconstruct the argument for such responsibility gaps to then argue that this argument is not sound. The argument assumes that commanders have no control over whether autonomous weapons inflict harm. I argue against this assumption. Although this investigation concerns a specific case of autonomous weapons systems, (...) I take steps towards vindicating the more general idea that superiors can be morally responsible in virtue of being in command. (shrink)

If climate change represents a severe threat to humankind, why then is response to it characterized by inaction at all levels? The authors argue there are two complementary explanations for the lack of motivation. First, our moral judgment system appears to be unable to identify climate change as an important moral problem and there are pervasive doubts about the agency of individuals. This explanation, however, is incomplete: Individual emitters can effectively be held morally responsible for their luxury emissions. Second, doubts (...) about individual agency have become overly emphasized and fail to convincingly exonerate individuals from responsibility. This book extends the second explanation for the motivational gap, namely that the arguments for the lack of individual agency do in fact correspond to mechanisms of moral disengagement. The use of these mechanisms enables consumption elites to maintain their consumptive lifestyles without having to accept moral responsibility for their luxury emissions. (shrink)

When artificial intelligence (AI) is used to make high-stakes decisions, some worry that this will create a morally troubling responsibility gap—that is, a situation in which nobody is morally responsible for the actions and outcomes that result. Since the responsibility gap might be thought to result from individuals lacking knowledge of the future behavior of AI systems, it can be and has been suggested that deploying explainable artificial intelligence (XAI) techniques will help us to avoid it. These techniques (...) provide humans with certain forms of understanding of the systems in question. In this paper, I consider whether existing XAI techniques can indeed close the responsibility gap. I identify a number of significant limits to their ability to do so. Ensuring that responsibility for AI-assisted outcomes is maintained may require using different techniques in different circumstances, and potentially also developing new techniques that can avoid each of the issues identified. (shrink)