With the increasing degrees A.I.s and machines are applied across different social contexts, the need for implementing ethics in A.I.s is pressing. In this paper, we argue for a pluralist hybrid model for the implementation of moral A.I.s. We first survey current approaches to moral A.I.s and their inherent limitations. Then we propose the pluralist hybrid approach and show how these limitations of moral A.I.s can be partly alleviated by the pluralist hybrid approach. The (...) core ethical decision-making capacity of an A.I. based on the pluralist hybrid approach consists of two systems. The first is a deterministic algorithm system that incorporates different moral rules for making explicit moral decisions. The second is a machine learning system that calculates the value of the variables required by the application of moral principles. The pluralist hybrid system is better than the existing proposals as (i) it better addresses the moral disagreement problem of the top-down approach by including different moral principles, while (ii) it reduces the opacity of ethical decision-making compared with a bottom-up system by implementing explicit moral principles for moral decision making. (shrink)

The use of opaque, uninterpretable artificial intelligence systems in health care can be medically beneficial, but it is often viewed as potentially morally problematic on account of this opacity—because the systems are black boxes. Alex John London has recently argued that opacity is not generally problematic, given that many standard therapies are explanatorily opaque and that we can rely on statistical validation of the systems in deciding whether to implement them. But is statistical validation sufficient to justify implementation (...) of these AI systems in health care, or is it merely one of the necessary criteria? I argue that accountability, which holds an important role in preserving the patient‐physician trust that allows the institution of medicine to function, contributes further to an account of AI system justification. Hence, I endorse the vanishing accountability principle: accountability in medicine, in addition to statistical validation, must be preserved. AI systems that introduce problematic gaps in accountability should not be implemented. (shrink)

This paper takes stock of all the various factors that cause the design-time opacity of autonomous systems behaviour. The factors include embodiment effects, design-time knowledge gap, human factors, emergent behaviour and tacit knowledge. This situation is contrasted with the usual representation of moral dilemmas that assume perfect information. Since perfect information is not achievable, the traditional moral dilemma representations are not valid and the whole problem of ethical autonomous systems design proves to be way more empirical (...) than previously understood. (shrink)

This chapter focuses on the connection between moral disagreement and moral relativism. Moral relativists, generally speaking, think both (i) that there is no unique objectively correct moral standard and (ii) that the rightness and wrongness of an action depends in some way on a moral standard accepted by some group or an individual. This chapter will first consider the metaphysical and epistemic arguments for moral relativism that begin from the premise that there is (...) considerable amount of moral disagreement both within individual societies and between them. The second half of the chapter, by contrast, focuses on the objection that moral relativism threatens to make us unable to have moral disagreements because it seems to make us speak past one another. This part of the chapter also evaluates relativist responses to this disagreement problem that rely on semantic opacity, disagreement in attitude, metalinguistic negotiations, and truth relativism. The chapter finally concludes by considering future directions of research in this area. (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)

Artificial intelligence (AI) and robotics are digital technologies that will have significant impact on the development of humanity in the near future. They have raised fundamental questions about what we should do with these systems, what the systems themselves should do, what risks they involve, and how we can control these. - After the Introduction to the field (§1), the main themes (§2) of this article are: Ethical issues that arise with AI systems as objects, i.e., tools made and used (...) by humans. This includes issues of privacy (§2.1) and manipulation (§2.2), opacity (§2.3) and bias (§2.4), human-robot interaction (§2.5), employment (§2.6), and the effects of autonomy (§2.7). Then AI systems as subjects, i.e., ethics for the AI systems themselves in machine ethics (§2.8) and artificial moral agency (§2.9). Finally, the problem of a possible future AI superintelligence leading to a “singularity” (§2.10). We close with a remark on the vision of AI (§3). - For each section within these themes, we provide a general explanation of the ethical issues, outline existing positions and arguments, then analyse how these play out with current technologies and finally, what policy consequences may be drawn. (shrink)

How should we design AI systems that make moral decisions that affect us? When there is disagreement about which moral decisions should be made and which methods would produce them, we should avoid arbitrary design choices. However, I show that this leads to a regress problem similar to the one metanormativists face involving higher orders of uncertainty. I argue that existing strategies for handling this parallel problem give verdicts about where to stop in the regress (...) that are either too arbitrary or too difficult to implement. I propose a new strategy for AI designers that is better than these alternatives. (shrink)

Artificially intelligent systems will be used to make increasingly important decisions about us. Many of these decisions will have to be made without universal agreement about the relevant moral facts. For other kinds of disagreement, it is at least usually obvious what kind of solution is called for. What makes moral disagreement especially challenging is that there are three different ways of handling it. _Moral solutions_ apply a moral theory or related principles and largely ignore (...) the details of the disagreement. _Compromise solutions_ apply a method of finding a compromise and taking information about the disagreement as input. _Epistemic solutions_ apply an evidential rule that treats the details of the disagreement as evidence of moral truth. Proposals for all three kinds of solutions can be found in the AI ethics and value alignment literature, but little has been said to justify choosing one over the other. I argue that the choice is best framed in terms of _moral risk_. (shrink)

Hybrid expressivists depart from pure expressivists by claiming that moral sentences express beliefs and desires. Daniel Boisvert and Michael Ridge, two prominent defenders of hybrid views, also depart from pure expressivists by claiming that moral sentences express general attitudes rather than an attitude towards the subject of the sentence. This article argues that even if the shift to general attitudes helps solve some of the traditional problems associated with pure expressivism, a view like Ridge's, according to (...) which the descriptive meaning is speaker relative, turns out to have problems explaining moral disagreement. (shrink)

The development of the systems of artificial intelligence (AI) and digital transformation in general lead to the formation of multitude of autonomous agents of artificial and mixed genealogy, as well as to complex structures in the information and regulatory environment with many opportunities and pathologies and a growing level of uncertainty in making managerial decisions. The situation is complicated by the continuing plurality of understanding of the essence of AI systems. The modern expanded understanding of AI goes back to ideas (...) formulated more than 100 years ago. In official national policy documents on the development of AI, working definitions of AI are preferred. The current stage of AI systems life cycle can be assessed as the completion of the initial period in the development of systems associated with weak AI. The ability of artificial systems to realize themselves as a separate person becomes one of the serious scientific and practical challenges. Attention to the issues of the ethics of AIS indicates the expansion of the diversity of its forms and the beginning of the work in the field of goal-setting. New moral and ethical problems also arise in connection with the possibility of the creation of genuine conscious subjects in the foreseeable future. There is an increasing phenomenon of degradation of natural intelligence. It is required to take into account the issue of the heterogeneity of data generated by humans, electronic sensors and network devices in the dynamic complex environments of the digital economy, the issue of the complexity of the process of co-evolution of AI systems, collective and individual natural consciousness. A special area of opportunities and risks is the development of neurotechnologies. The object of control is digital twins, through which there can be manipulation of real attitudes, preferences, and behavior of individuals. As a result, there are the development of technological capabilities that provoke destructive phenomena as well as the formation of a new class of mass addictions. (shrink)

As artificial intelligence systems are becoming increasingly autonomous and will soon be able to make decisions on their own about what to do, AI researchers have started to talk about the need to align AI with human values. The AI ‘value alignment problem’ faces two kinds of challenges—a technical and a normative one—which are interrelated. The technical challenge deals with the question of how to encode human values in artificial intelligence. The normative challenge is associated with two questions: “Which (...) values or whose values should artificial intelligence align with?” My concern is that AI developers underestimate the difficulty of answering the normative question. They hope that we can easily identify the purposes we really desire and that they can focus on the design of those objectives. But how are we to decide which objectives or values to induce in AI, given that there is a plurality of values and moral principles and that our everyday life is full of moral disagreements? In my paper I will show that although it is not realistic to reach an agreement on what we, humans, really want as people value different things and seek different ends, it may be possible to agree on what we do not want to happen, considering the possibility that intelligence, equal to our own, or even exceeding it, can be created. I will argue for pluralism which is compatible with objectivism. In spite of the fact that there is no uniquely best solution to every moral problem, it is still possible to identify which answers are wrong. And this is where we should begin the value alignment of AI. (shrink)

How should we design AI systems that make moral decisions that affect us? When there is disagreement about which moral decisions should be made and which methods would produce them, we should avoid arbitrary design choices. However, I show that this leads to a regress problem similar to the one metanormativists face involving higher orders of uncertainty. I argue that existing strategies for handling this parallel problem give verdicts about where to stop in the regress (...) that are either too arbitrary or too difficult to implement. I propose a new strategy for AI designers that is better than these alternatives. (shrink)

In their paper ‘Responsibility, second opinions and peer-disagreement: ethical and epistemological challenges of using AI in clinical diagnostic contexts’, Kempt and Nagel discuss the use of medical AI systems and the resulting need for second opinions by human physicians, when physicians and AI disagree, which they call the rule of disagreement.1 The authors defend RoD based on three premises: First, they argue that in cases of disagreement in medical practice, there is an increased burden of proof for (...) the physician in charge, to defend why the opposing view is adopted or overridden. This burden for justification can be understood as an increased responsibility. In contrast, such burden does allegedly not arise, when physicians agree in their judgement. Second, in those medical contexts where humans collaborate with humans such justification can be provided, since human experts can discuss the evidence and reasons that have led them to their judgement, through which the sources of disagreement can be found and a justified decision can be made by the physician in charge. Third, unlike human-to-human collaboration, such communicative exchange is not possible with an AI system. Due to AI’s opacity, the physician in charge has no means of illuminating why the AI disagrees. Conclusively, the authors propose RoD as a solution. RoD suggests that a second human expert should be consulted for advise in cases of human–AI disagreement. Once AI systems become more widespread in clinical practice, it can be expected that such type of disagreement occurs more frequently. AI, after all, is being implemented, because it promises, among others, higher accuracy, which implies that some abnormalities will be detected that the physician would have missed.2 Hence, it is laudable to discuss the moral implications of …. (shrink)

As artificial intelligence (AI) continues to advance, one key challenge is ensuring that AI aligns with certain values. However, in the current diverse and democratic society, reaching a normative consensus is complex. This paper delves into the methodological aspect of how AI ethicists can effectively determine which values AI should uphold. After reviewing the most influential methodologies, we detail an intuitionist research agenda that offers guidelines for aligning AI applications with a limited set of reliable moral intuitions, each underlying (...) a refined cooperative view of AI. We discuss appropriate epistemic tools for collecting, filtering, and justifying moral intuitions with the aim of reducing cognitive and social biases. The proposed methodology facilitates a large collective participation in AI alignment, while ensuring the reliability of the considered moral judgments. (shrink)

In this paper we analyse the conditions for attributing to AI autonomous systems the ontological status of “artificial moral agents”, in the context of the “distributed responsibility” between humans and machines in Machine Ethics (ME). In order to address the fundamental issue in ME of the unavoidable “opacity” of their decisions with ethical/legal relevance, we start from the neuroethical evidence in cognitive science. In humans, the “transparency” and then the “ethical accountability” of their actions as responsible moral (...) agents is not in contradiction with the unavoidable “opacity” (unawareness) of the brain process by which they perform their moral judgements on the right action to execute. In fact, the moral accountability of our actions depends on what is immediately before and after our “moral judgements” on the right action to execute (formally, deontic first order logic (FOL) decisions). I.e., our moral accountability depends on the “ethical constraints” we imposed to our judgement before performing it in an opaque way. Anyway, our moral accountability depends overall on the “ethical assessment” or explicit “moral reasoning” after and over the moral judgement before executing our actions (deontic higher order logic (HOL) assessment). In this way, in the light of the AI “imitation game”, the consistent attribution of the status of ethically accountable artificial moral agents to autonomous AI systems depends on two similar conditions. Firstly, it depends on the presence of “ethical constraints” to be satisfied in their Machine Learning (ML) supervised optimization algorithm during its training phase, to give the system ethical skills (“competences”) in its decisions. Secondly – and definitely—, it depends on the presence in an AI autonomous system of a deontic HOL “ethical reasoner” to perform an automatic, and fully transparent assessment (metalogical deontic valuation) about the decisions taken by the ethically skilled ML algorithm about the right action to execute, before executing it. Finally, we show that the proper deontic FOL and HOL for this class of artificial moral agents is Kripke’s modal relational logic, in its algebraic topological formalization. This is naturally implemented in the dissipative QFT unsupervised deep learning of our brains, based on the “doubling of the degrees of freedom” (DDF), and then in the so-called “deep-belief” artificial neural networks for the statistical data pre-processing. This unsupervised learning procedure is also compliant with the usage of the “maximin fairness principle”, used as a balancing aggregation principle of the statistical variables in Sen’s formal theory of fairness. (shrink)

Several proposals for moral enhancement would use AI to augment (auxiliary enhancement) or even supplant (exhaustive enhancement) human moral reasoning or judgment. Exhaustive enhancement proposals conceive AI as some self-contained oracle whose superiority to our own moral abilities is manifest in its ability to reliably deliver the ‘right’ answers to all our moral problems. We think this is a mistaken way to frame the project, as it presumes that we already know many things that we are (...) still in the process of working out, and reflecting on this fact reveals challenges even for auxiliary proposals that eschew the oracular approach. We argue there is nonetheless a substantial role that ‘AI mentors’ could play in our moral education and training. Expanding on the idea of an AI Socratic Interlocutor, we propose a modular system of multiple AI interlocutors with their own distinct points of view reflecting their training in a diversity of concrete wisdom traditions. This approach minimizes any risk of moral disengagement, while the existence of multiple modules from a diversity of traditions ensures pluralism is preserved. We conclude with reflections on how all this relates to the broader notion of moral transcendence implicated in the project of AI moral enhancement, contending it is precisely the whole concrete socio-technical system of moral engagement that we need to model if we are to pursue moral enhancement. (shrink)

Recent work in AI ethics has come to bear on questions of responsibility. Specifically, questions of whether the nature of AI-based systems render various notions of responsibility inappropriate. While substantial attention has been given to backward-looking senses of responsibility, there has been little consideration of forward-looking senses of responsibility. This paper aims to plug this gap, and will concern itself with responsibility as moral obligation, a particular kind of forward-looking sense of responsibility. Responsibility as moral obligation is predicated (...) on the idea that agents have at least some degree of control over the kinds of systems they create and deploy. AI systems, by virtue of their ability to learn from experience once deployed, and their often experimental nature, may therefore pose a significant challenge to forward-looking responsibility. Such systems might not be able to have their course altered, and so even if their initial programming determines their goals, the means by which they achieve these goals may be outside the control of human operators. In cases such as this, we might say that there is a gap in moral obligation. However, in this paper, I argue that there are no “gaps” in responsibility as moral obligation, as this question comes to bear on AI systems. I support this conclusion by focusing on the nature of risks when developing technology, and by showing that technological assessment is not only about the consequences that a specific technology might have. Technological assessment is more than merely consequentialist, and should also include a hermeneutic component, which looks at the societal meaning of the system. Therefore, while it may be true that the creators of AI systems might not be able to fully appreciate what the consequences of their systems might be, this does not undermine or render improper their responsibility as moral obligation. (shrink)

New AI technologies have the potential to cause unintended harms in diverse domains including warfare, judicial sentencing, biomedicine and governance. One strategy for realising the benefits of AI whilst avoiding its potential dangers is to ensure that new AIs are properly ‘aligned’ with some form of ‘alignment target.’ One danger of this strategy is that – dependent on the alignment target chosen – our AIs might optimise for objectives that reflect the values only of a certain subset of society, and (...) that do not take into account alternative views about what constitutes desirable and safe behaviour for AI agents. In response to this problem, several AI ethicists have suggested alignment targets that are designed to be sensitive to widespread normative disagreement amongst the relevant stakeholders. Authors inspired by voting theory have suggested that AIs should be aligned with the verdicts of actual or simulated ‘moral parliaments’ whose members represent the normative views of the relevant stakeholders. Other authors inspired by decision theory and the philosophical literature on moral uncertainty have suggested that AIs should maximise socially expected choiceworthiness. In this paper, I argue that both of these proposals face several important problems. In particular, they fail to select attractive ‘compromise options’ in cases where such options are available. I go on to propose and defend an alternative, bargaining-theoretic alignment target, which avoids the problems associated with the voting- and decision-theoretic approaches. (shrink)

Morality is a normative system of guidance that figures into practical reason by telling people what to do in various situations. The problem, however, is that morality has inherent gaps that often render it inefficacious. First, it may be indeterminate due to the high level of generality in which its principles are formulated. Second, moral terms such as ‘good’ and ‘right’ may be so vague that they fail to specify the requisite behavior. And third, its subjective aspect, which (...) is a product of personal experience, generates moral disagreement and thereby creates coordination problems that frustrate society’s collective moral aims. The objective of this article is to advance the thesis that morality must sometimes depend on law as a supplementary source of practical reason, a dependence which can be explained in terms of three essential features of law: its institutional character, its claim to authority, and its status as a second-order exclusionary reason for action. It shall then be explained how these three features enable law to make difficult decisions on behalf of individuals, define objective standards of conduct, and solve coordination problems, respectively, and in doing so, manage to fill in the gaps of morality mentioned above. Hence, it will be argued that law is also a normative system that helps people achieve their moral aims, notwithstanding the fact that it guides human behavior through a different logic and mode of operation from those of morality. (shrink)

Within current debates about the future impact of Artificial Intelligence on human society, roughly three different perspectives can be recognised: the technology-centric perspective, claiming that AI will soon outperform humankind in all areas, and that the primary threat for humankind is superintelligence; the human-centric perspective, claiming that humans will always remain superior to AI when it comes to social and societal aspects, and that the main threat of AI is that humankind’s social nature is overlooked in technological designs; and the (...) collective intelligence-centric perspective, claiming that true intelligence lies in the collective of intelligent agents, both human and artificial, and that the main threat for humankind is that technological designs create problems at the collective, systemic level that are hard to oversee and control. The current paper offers the following contributions: a clear description for each of the three perspectives, along with their history and background; an analysis and interpretation of current applications of AI in human society according to each of the three perspectives, thereby disentangling miscommunication in the debate concerning threats of AI; and a new integrated and comprehensive research design framework that addresses all aspects of the above three perspectives, and includes principles that support developers to reflect and anticipate upon potential effects of AI in society. (shrink)

The increasing demand for transparency in AI has recently come under scrutiny. The question is often posted in terms of “epistemic double standards”, and whether the standards for transparency in AI ought to be higher than, or equivalent to, our standards for ordinary human reasoners. I agree that the push for increased transparency in AI deserves closer examination, and that comparing these standards to our standards of transparency for other opaque systems is an appropriate starting point. I suggest that a (...) more fruitful exploration of this question will involve a different comparison class. We routinely treat judgments made by highly trained experts in specialized fields as fair or well-grounded even though—by the nature of expert/layperson division of epistemic labor—an expert will not be able to provide an explanation of the reasoning behind these judgments that makes sense to most other people. Regardless, laypeople are thought to be acting reasonably—and ethically—in deferring to the judgments of experts that concern their areas of specialization. I suggest that we reframe our question regarding the appropriate standards of transparency in AI as one that asks when, why, and to what degree it would be ethical to accept opacity in AI. I argue that our epistemic relation to certain opaque AI technology may be relevantly similar to the layperson’s epistemic relation to the expert in certain respects, such that the successful expert/layperson division of epistemic labor can serve as a blueprint for the ethical use of opaque AI. (shrink)

A morally acceptable course of AI development should avoid two dangers: creating unaligned AI systems that pose a threat to humanity and mistreating AI systems that merit moral consideration in their own right. This paper argues these two dangers interact and that if we create AI systems that merit moral consideration, simultaneously avoiding both of these dangers would be extremely challenging. While our argument is straightforward and supported by a wide range of pretheoretical moral judgments, it has (...) far-reaching moral implications for AI development. Although the most obvious way to avoid the tension between alignment and ethical treatment would be to avoid creating AI systems that merit moral consideration, this option may be unrealistic and is perhaps fleeting. So, we conclude by offering some suggestions for other ways of mitigating mistreatment risks associated with alignment. (shrink)

The emergence of AI is posing serious challenges to standard conceptions of moral status. New non-biological entities are able to act and make decisions rationally. The question arises, in this regard, as to whether AI systems possess or can possess the necessary properties to be morally considerable. In this chapter, we have undertaken a systematic analysis of the various debates that are taking place about the moral status of AI. First, we have discussed the possibility that AI systems, (...) by virtue of its new agential capabilities, can be understood as a moral agent. Discussions between those defending mentalist and anti-mentalist positions have revealed many nuances and particularly relevant theoretical aspects. Second, given that an AI system can hardly be an entity qualified to be responsible, we have delved into the responsibility gap and the different ways of understanding and addressing it. Third, we have provided an overview of the current and potential patientist capabilities that AI systems possess. This has led us to analyze the possibilities of AI possessing moral patiency. In addition, we have addressed the question of the moral and legal rights of AI. Finally, we have introduced the two most relevant authors of the relational turn on the moral status of AI, Mark Coeckelbergh and David Gunkel, who have been led to defend a relational approach to moral life as a result of the problems associated with the ontological understanding of moral status. (shrink)

On virtue ethical grounds, Barbro Fröding and Martin Peterson recently recommended that near-future AIs should be developed as ‘Friendly AI’. AI in social interaction with humans should be programmed such that they mimic aspects of human friendship. While it is a reasonable goal to implement AI systems interacting with humans as Friendly AI, I identify four issues that need to be addressed concerning Friendly AI with Fröding’s and Peterson’s understanding of Friendly AI as a starting point. In a first step, (...) I briefly recapitulate Fröding’s and Peterson’s arguments for Friendly AI. I then highlight some issues with Fröding’s and Peterson’s approach and line of reasoning and identify four problems related to the notion of Friendly AI, which all pertain to the role and need for humans’ moral development. These are that one should consider the moral tendencies and preferences of the humans interacting with a friendly AI, it needs to be considered whether the humans interacting with a Friendly AI are still developing their virtues and character traits, the indirect effects of replacing humans with Friendly AI should be considered with respect to the possibilities for humans to develop their moral virtues and that the question whether the AI is perceived as some form of Artificial General Intelligence cannot be neglected. In conclusion, I argue that all of these four problems are related to humans moral development and that this observation strongly emphasizes the role and need for humans moral development in correlation to the accelerating development of AI-systems. (shrink)

A principal goal of the discipline of artificial morality is to design artificial agents to act as if they are moral agents. Intermediate goals of artificial morality are directed at building into AI systems sensitivity to the values, ethics, and legality of activities. The development of an effective foundation for the field of artificial morality involves exploring the technological and philosophical issues involved in making computers into explicit moral reasoners. The goal of this paper is to discuss strategies (...) for implementing artificial morality and the differing criteria for success that are appropriate to different strategies. (shrink)

The threat from possible future superintelligent AI has given rise to discussion of the so-called “value alignment problem”. This is the problem of how to ensure artificially intelligent systems align with human values, and thus (hopefully) mitigate risks associated with them. Naturally, AI value alignment is often discussed in relation to morally relevant values, such as the value of human lives or human wellbeing. However, solutions to the value alignment problem target all human values, not only morally (...) relevant ones. Is there a value alignment problem in other domains? In this paper, I explore whether the AI value alignment problem extends beyond morally relevant values to include aesthetic values. I demonstrate that the value alignment problem as typically framed includes aesthetic values, and, using examples from computer vision, put forward that AI may be misaligned with human values in the artistic realm. Whilst misalignment may be cause for concern when considering AI creativity, I argue that aesthetic value is a case in which we do not want AI to be fully aligned with human values. In doing so, I offer support to Peterson’s moderate value alignment thesis. (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)

Many artificial intelligence (AI) systems currently used for decision-making are opaque, i.e., the internal factors that determine their decisions are not fully known to people due to the systems’ computational complexity. In response to this problem, several researchers have argued that human decision-making is equally opaque and since simplifying, reason-giving explanations (rather than exhaustive causal accounts) of a decision are typically viewed as sufficient in the human case, the same should hold for algorithmic decision-making. Here, I contend that this (...) argument overlooks that human decision-making is sometimes significantly more transparent and trustworthy than algorithmic decision-making. This is because when people explain their decisions by giving reasons for them, this frequently prompts those giving the reasons to govern or regulate themselves so as to think and act in ways that confirm their reason reports. AI explanation systems lack this self-regulative feature. Overlooking it when comparing algorithmic and human decision-making can result in underestimations of the transparency of human decision-making and in the development of explainable AI that may mislead people by activating generally warranted beliefs about the regulative dimension of reason-giving. (shrink)

The development of machine learning and deep learning (DL) in the field of AI (artificial intelligence) is the direct result of the advancement of nano-electronics. Machine learning is a function that provides the system with the capacity to learn from data without being programmed explicitly. It is basically a mathematical and probabilistic model. DL is part of machine learning methods based on artificial neural networks, simply called neural networks (NNs), as they are inspired by the biological NNs that constitute organic (...) brains. Despite its similarity to biological organs such as human brains, major problems arise in trying to attribute moral responsibility to autonomic systems based on hardware including nano-electronic devices, which are sought to replace humans (moral agents) in the context of AI. It is suggested that the required emotional environment which enables actions according to reasons in humans is not witnessed at AI devices. Though AI technology raises an enticing resemblance to human actions, this resemblance is to be considered with a skeptical eye. It is because actions are associated with reasons while causes are connected to operations. Human agents are capable of acting upon their reasons, while AI devices are limited only to operations as they are conditioned by their programming, which is considered as an embodiment of some causes. As moral responsibility goes hand in hand with the capacity to act (according to reasons), attributing moral responsibility to AI devices is revealed to be but a misleading metaphor. (shrink)

Many have recently argued that there are weighty reasons against making high-stakes decisions solely on the basis of recommendations from artificially intelligent (AI) systems. Even if deference to a given AI system were known to reliably result in the right action being taken, the argument goes, that deference would lack morally important characteristics: the resulting decisions would not, for instance, be based on an appreciation of right-making reasons. Nor would they be performed from moral virtue; nor would they have (...) moral worth. I argue that, even if these characteristics all have intrinsic value, that intrinsic value has no practical relevance to decisions about whether to defer to AI. I make that point by drawing on a lesson from the literature on moral testimony. Once it is granted that deference to a reliable source is the policy most likely to bring about right action, a refusal to defer carries with it a heightened risk of wronging and mistreating people. And that heightened risk of wrongdoing, I argue, cannot be justified by appeal to the intrinsic value of striving for a morally exemplary decision-making process. (shrink)

In The Moral Problem, Smith defended an analysis of moral judgments based on a number of platitudes about morality. The platitudes are supposed to constitute conceptual constraints which an analysis of moral terms must capture “on pain of not being an analysis of moral terms at all”. This paper discusses this philosophical methodology in light of the fact that the propositions identified as platitudes are not obvious truths – they are propositions we can be uncertain (...) about. This, we argue, is a kind of fundamental philosophical uncertainty, and we develop an account of fundamental uncertainty (for both philosophical and other issues). The key to understand such uncertainty, on our view, is conceptual opacity – i.e., that the content and reference of concepts is not necessarily transparent to competent concept users. We argue that Smith’s own view of conceptual analysis in TMP provides one plausible explanation of fundamental uncertainty. However, we also argue that another potential explanation is conceptual indeterminacy. If some fundamental philosophical uncertainties are best explained in this way, the implication is that there is no determinately correct analysis of the target terms and concepts. (shrink)

Who is responsible when an AI machine causes something to go wrong? Or is there a gap in the ascription of responsibility? Answers range from claiming there is a unique responsibility gap, several different responsibility gaps, or no gap at all. In a nutshell, the problem is as follows: on the one hand, it seems fitting to hold someone responsible for a wrong caused by an AI machine; on the other hand, there seems to be no fitting bearer of (...) responsibility for this wrong. In this article, we focus on a particular (aspect of the) AI responsibility gap: it seems fitting that someone should bear the legal consequences in scenarios involving AI machines with design defects; however, there seems to be no such fitting bearer. We approach this problem from the legal perspective, and suggest vicarious liability of AI manufacturers as a solution to this problem. Our proposal comes in two variants: the first one has a narrower range of application, but can be easily integrated in current legal frameworks; the second one requires a revision of current legal frameworks, but has a wider range of application. The latter variant employs a broadened account of vicarious liability. We emphasise strengths of the two variants and finally highlight how vicarious liability offers important insights for addressing a moral AI responsibility gap. (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.

Moral uncertainty surrounding the use of armed drones has been a persistant problem for more than two decades. In response, [this book] provides greater clarity by investigating the ways in which violent drone use is seen as just or unjust in a variety of circumstances. Adopting a broad-based approach to normative inquiry, this book organizes moral ideas around a series of concepts of drone violence, including warfare, violent law enforcement, tele-intimate violence and violence devolved from humans to (...) artificial intelligence (AI) technologies. Christian Enemark assesses these concepts to discern and address teh potential for armed drone systems to support moral progress or to generate injustice. (shrink)

This paper explores the role and resolution of disagreements between physicians and their diagnostic AI-based decision support systems. With an ever-growing number of applications for these independently operating diagnostic tools, it becomes less and less clear what a physician ought to do in case their diagnosis is in faultless conflict with the results of the DSS. The consequences of such uncertainty can ultimately lead to effects detrimental to the intended purpose of such machines, e.g. by shifting the burden of proof (...) towards a physician. Thus, we require normative clarity for integrating these machines without affecting established, trusted, and relied upon workflows. In reconstructing different causes of conflicts between physicians and their AI-based tools—inspired by the approach of “meaningful human control” over autonomous systems and the challenges to resolve them—we will delineate normative conditions for “meaningful disagreements”. These incorporate the potential of DSS to take on more tasks and outline how the moral responsibility of a physician can be preserved in an increasingly automated clinical work environment. (shrink)

There are many studies conducted on recommendation systems, most of which are focused on recommending items to users and vice versa. Nowadays, social networks are complicated due to carrying vast arrays of data about individuals and organizations. In today’s competitive environment, companies face two significant problems: supplying resources and attracting new customers. Even the concept of supply-chain management in a virtual environment is changed. In this article, we propose a new and innovative combination approach to recommend organizational people in social (...) networks based on organizational communication and SCM. The proposed approach uses a hybrid strategy that combines basic collaborative filtering and demographic recommendation systems, using data mining, artificial neural networks, and fuzzy techniques. The results of experiments and evaluations based on a real dataset collected from the LinkedIn social network showed that the hybrid recommendation system has higher accuracy and speed than other essential methods, even substantially has eliminated the fundamental problems with such systems, such as cold start, scalability, diversity, and serendipity. (shrink)

Just as humans can draw conclusions responsibly or irresponsibly, so too can computers. Machine learning systems that have been trained on data sets that include irresponsible judgments are likely to yield irresponsible predictions as outputs. In this paper I focus on a particular kind of inference a computer system might make: identification of the intentions with which a person acted on the basis of photographic evidence. Such inferences are liable to be morally objectionable, because of a way in which they (...) are presumptuous. After elaborating this moral concern, I explore the possibility that carefully procuring the training data for image recognition systems could ensure that the systems avoid the problem. The lesson of this paper extends beyond just the particular case of image recognition systems and the challenge of responsibly identifying a person’s intentions. Reflection on this particular case demonstrates the importance (as well as the difficulty) of evaluating machine learning systems and their training data from the standpoint of moral considerations that are not encompassed by ordinary assessments of predictive accuracy. (shrink)

Endowing artificial systems with explanatory capacities about the reasons guiding their decisions, represents a crucial challenge and research objective in the current fields of Artificial Intelligence (AI) and Computational Cognitive Science [Langley et al., 2017]. Current mainstream AI systems, in fact, despite the enormous progresses reached in specific tasks, mostly fail to provide a transparent account of the reasons determining their behavior (both in cases of a successful or unsuccessful output). This is due to the fact that the classical (...) class='Hi'>problem of opacity in artificial neural networks (ANNs) explodes with the adoption of current Deep Learning techniques [LeCun, Bengio, Hinton, 2015]. In this paper we argue that the explanatory deficit of such techniques represents an important problem, that limits their adoption in the cognitive modelling and computational cognitive science arena. In particular we will show how the current attempts of providing explanations of the deep nets behaviour (see e.g. [Ritter et al. 2017] are not satisfactory. As a possibile way out to this problem, we present two different research strategies. The first strategy aims at dealing with the opacity problem by providing a more abstract interpretation of neural mechanisms and representations. This approach is adopted, for example, by the biologically inspired SPAUN architecture [Eliasmith et al., 2012] and by other proposals suggesting, for example, the interpretation of neural networks in terms of the Conceptual Spaces framework [Gärdenfors 2000, Lieto, Chella and Frixione, 2017]. All such proposals presuppose that the neural level of representation can be considered somehow irrelevant for attacking the problem of explanation [Lieto, Lebiere and Oltramari, 2017]. In our opinion, pursuing this research direction can still preserve the use of deep learning techniques in artificial cognitive models provided that novel and additional results in terms of “transparency” are obtained. The second strategy is somehow at odds with respect to the previous one and tries to address the explanatory issue by avoiding to directly solve the “opacity” problem. In this case, the idea is that one of resorting to pre-compiled plausible explanatory models of the word used in combination with deep-nets (see e.g. [Augello et al. 2017]). We argue that this research agenda, even if does not directly fits the explanatory needs of Computational Cognitive Science, can still be useful to provide results in the area of applied AI aiming at shedding light on the models of interaction between low level and high level tasks (e.g. between perceptual categorization and explanantion) in artificial systems. (shrink)

Mozart's great opera, Don Giovanni, poses a number of significant philosophical and aesthetic challenges, and yet it remains, for the most part, little discussed by contemporary philosophers. A notable exception to this is Bernard Williams's important paper, ‘Don Juan as an Idea’, which contains an illuminating discussion of Kierkegaard's ground-breaking interpretation of the opera, ‘The Immediate Erotic Stages or the Musical-Erotic’, in Either/Or. Kierkegaard's pseudonymous author's approach here is, in some respects, reminiscent of a currently rather fashionable narrative-inspired moral (...) philosophy, of which Williams himself is perhaps the most impressive recent exponent. In the light of this apparent methodological confluence, Williams's disagreement with A about the meaning of Don Giovanni's final two scenes seems particularly significant. By offering an interpretation of Don Giovanni that both retains A's fundamental ideas and manages to get round the problems in Williams's account, I will show that the greatness of Mozart's opera is largely a function of the challenge it presents to the ‘morality system’. (shrink)

Artificial intelligence tools used in employment decision-making cut across the multiple stages of job advertisements, shortlisting, interviews and hiring, and actual and potential bias can arise in each of these stages. One major challenge is to mitigate AI bias and promote fairness in opaque AI systems. This paper argues that the equal opportunity merit principle is an ethical approach for fair AI employment decision-making. Further, explainable AI can mitigate the opacity problem by placing greater emphasis on enhancing the (...) understanding of reasonable users and affected persons as to the AI output. Both the equal opportunity merit principle and explainable AI should be integrated in the design and implementation of AI employment decision-making systems so as to ensure, as far as possible, that the AI output is arrived at through a fair process. (shrink)

Our ability to locate moral responsibility is often thought to be a necessary condition for conducting morally permissible medical practice, engaging in a just war, and other high-stakes endeavors. Yet, with increasing reliance upon artificially intelligent systems, we may be facing a wideningresponsibility gap, which, some argue, cannot be bridged by traditional concepts of responsibility. How then, if at all, can we make use of crucial emerging technologies? According to Colin Allen and Wendell Wallach, the advent of so-called ‘artificial (...) moral agents’ (AMAs) is inevitable. Still, this notion may seem to push back the problem, leaving those who have an interest in developing autonomous technology with a dilemma. We may need to scale-back our efforts at deploying AMAs (or at least maintain human oversight); otherwise, we must rapidly and drastically update our moral and legal norms in a way that ensures responsibility for potentially avoidable harms. This paper invokes contemporary accounts of responsibility in order to show how artificially intelligent systems might be held responsible. Although many theorists are concerned enough to develop artificial conceptions ofagencyor to exploit our present inability to regulate valuable innovations, the proposal here highlights the importance of—and outlines a plausible foundation for—a workable notion ofartificial moral responsibility. (shrink)

: Autonomous driving systems promise important changes for future of transport, primarily through the reduction of road accidents. However, ethical concerns, in particular, two central issues, will be key to their successful development. First, situations of risk that involve inevitable harm to passengers and/or bystanders, in which some individuals must be sacrificed for the benefit of others. Secondly, and identification responsible parties and liabilities in the event of an accident. Our work addresses the first of these ethical problems. We are (...) interested in investigating how humans respond to critical situations and what reactions they consider to be morally right or at least preferable to others. Our experimental approach relies on the trolley dilemma and knowledge gained from previous research on this. More specifically, our main purpose was to test the difference between what human drivers actually decide to do in an emergency situations whilst driving a realistic simulator and the moral choices they make when they pause to consider what they would do in the same situation and to better understand why these choices may differs. Keywords: Self-driving Cars; Trolley Problem; Moral Choices; Moral Responsibility; Virtual Reality Dilemmi morali nelle automobili a guida autonoma Riassunto: I sistemi di guida autonomi promettono importanti cambiamenti per il futuro dei trasporti, principalmente attraverso la riduzione degli incidenti stradali. Tuttavia, vi sono preoccupazioni etiche, in particolare due questioni centrali, fondamentali per il loro sviluppo. In primo luogo, le situazioni di rischio che comportano inevitabili danni ai passeggeri e/o ai pedoni, ovvero situazioni in cui alcune persone devono essere sacrificate a beneficio di altri. In secondo luogo, l’identificazione delle parti responsabili in caso di incidente. Il nostro lavoro affronta il primo di questi problemi etici. Siamo interessati a studiare come gli umani rispondono a situazioni critiche e quali reazioni considerano moralmente giuste o almeno preferibili. Il nostro approccio sperimentale si basa sul trolley problem e sulle conoscenze acquisite da precedenti ricerche su questo ambito. Più specificamente, il nostro scopo principale è quello di testare la differenza tra ciò che i conducenti umani decidono effettivamente di fare in una situazione di emergenza, mentre guidano un simulatore realistico, e le scelte morali che compiono se posti nella stessa situazione e hanno la possibilità di decidere senza limiti di tempo. Lo scopo è inoltre comprendere come e perché queste scelte possono differire. Parole chiave: Automobili a guida autonoma; Trolley problem; Scelte morali; Responsabilità morale, Realtà virtuale. (shrink)

Tae Wan Kim, John Hooker, and Thomas Donaldson make an attempt, in recent articles, to solve the alignment problem. As they define the alignment problem, it is the issue of how to give AI systems moral intelligence. They contend that one might program machines with a version of Kantian ethics cast in deontic modal logic. On their view, machines can be aligned with human values if such machines obey principles of universalization and autonomy, as well as a (...) deontic utilitarian principle. Programming machines to do so might be useful, in their view, for applications such as future autonomous vehicles. Their proposal draws both on traditional logic-based and contemporary connectionist approaches, to fuse factual information with normative principles. I will argue that this approach makes demands of machines that go beyond what is currently feasible, and may extend past the limits of the possible for AI. I also argue that a deontological ethics for machines should place greater stress on the formula of humanity of the Kantian categorical imperative. On this principle, one ought never treat a person as a mere means. Recognition of what makes a person a person requires ethical insight. Similar insight is needed to tell treatment as a means from treatment as a mere means. The resources in Kim, Hooker, and Donaldson’s approach is insufficient for this reason. Hesitation regarding deployment of autonomous machines is warranted in light of these alignment concerns. (shrink)

Moral problems do not always come in the form of great social controversies. More often, the moral decisions we make are made quietly, constantly, and within the context of everyday activities and quotidian dilemmas. Indeed, these smaller decisions are based on a moral foundation that few of us ever stop to think about but which guides our every action. Here distinguished philosopher Bernard Gert presents a clear and concise introduction to what he calls "common morality" -- the (...) moral system that most thoughtful people implicitly use when making everyday, common sense moral decisions and judgments. Common Morality is useful in that -- while not resolving every disagreement on controversial issues -- it is able to distinguish between acceptable and unacceptable answers to moral problems. In the first part of the book Gert lays out the fundamental features of common morality: moral rules, moral ideals, and a two-step procedure for determining when a violation of a moral rule is justified. Written in a non-technical style, the ten general moral rules include rules on which everyone can agree, such as "do not kill," "do not deceive," and "keep your promises." The moral ideals include similarly uncontroversial precepts such as "Relieve pain" and "Aid the needy." In the second part of the book Gert examines the underlying concepts that justify common morality, such as the notions of rationality and impartiality. The distillation of over 40 years of scholarship, this book is the most accessible version of Gert's influential theory of morality as well as an eye-opening look at the moral foundations of our everyday actions. Throughout the discussion is clear enough for a reader with little or no philosophy background. (shrink)

The combination of spirituality, meditation, and artificial intelligence (AI) has promising potential to expand people’s well-being using technology-based meditation. Proper meditation originates from Zen Buddhism and Patanjali’s Yoga Sutras and focuses on inner peace and intensified consciousness which elective personal disposition. AI, in turn, brings master means of delivering those practices in the form of self-improving systems that customize and make access to them easier. However, such an integration brings major philosophical and ethical issues into question, including the genuineness of (...) experiences that are facilitated by artificial intelligence, data sharing, concerns over over-dependence on the technology that may in turn cause reduced personal responsibility and hard work. This paper aims at analysing the critical integration of AI-driven meditation following the spiritual interpretations of traditional meditation without compromising the tenets of meditation. It presents an interdisciplinary approach based on recent findings from the field of cognitive science, moral AI, and Eastern wisdom traditions to approach these problems. Therefore, by identifying the research lacunae, it provides a groundwork for voting ethically in the integration of AI in mindfulness practice and avoiding constraining human-oriented values resulting in improved existential spiritual change. (shrink)

In 2018, three journalists accused one of the Members of the Russian Parliament of harassment at workplace. Many influential persons of the Russian elite engaged themselves in the public discussion of the conflict. We studied that high-profiled discussion using a hybrid method merging human- and logic-oriented approaches in argumentation studies. The method develops ideas of the new dialectics, the argumentation logic and the logical-cognitive approach to argumentation, on which is based the algorithm for determining of dispute resolution by aggregating (...) formal and informal tools of analysis. We have reconstructed the discussion as two disputes about questions A and B. A: Did the MP violate the code of conduct by making statements or actions against the journalists? B: Are actions like the behavior of the MP harassment? The opinions of the discussion participants were grouped into the four points of view: A1 – the MP did not violate the code of conduct, A2 – the MP violated the code of conduct, B3 – the actions are not harassment, B4 – the actions are harassment. We mapped arguments in support or against each of them using OVA software, evaluated the arguments with the help of the critical questions, a tool proposed in the new dialectics, and determined the ultimate A + B resolution by applying of the algorithm that combines elements of gradual and labelling semantics from the argumentation logic and the classification of disputes from the dialectical approaches. The resolution was a subset of four arguments that ensured the victory of A1+B4. However, the substantial incompatibility of those arguments highlighted a deep disagreement, an unresolvable difference of opinion, between the parties about the permissibility of courtship. The deep disagreement, a bonus result yielded by the application of the hybrid method, excluded the interpretation of the determined resolution as convincing for the parties, but pointed out a way to smooth the difference of opinions by elaborating of legal, social and moral aspects of the problem of harassment at workplace. (shrink)

Because of its practical advantages, machine learning (ML) is increasingly used for decision-making in numerous sectors. This paper demonstrates that the integral characteristics of ML, such as semi-autonomy, complexity, and non-deterministic modeling have important ethical implications. In particular, these characteristics lead to a lack of insight and lack of comprehensibility, and ultimately to the loss of human control over decision-making. Errors, which are bound to occur in any decision-making process, may lead to great harm and human rights violations. It is (...) important to have a principled way of assigning responsibility for such errors. The integral characteristics of ML, however, pose serious difficulties in defining responsibility and regulating ML decision-making. First, we elaborate on these characteristics and their epistemic and ethical implications. We then analyze possible general strategies for resolving the assignment of moral responsibility and show that, due to the specific way in which ML functions, each potential solution is problematic, whether we assign responsibility to humans, machines, or using hybrid models. Then, we shift focus on an alternative approach that bypasses moral responsibility and attempts to define legal liability independently through solutions such as informed consent and the no-fault compensation system. Both of these solutions prove unsatisfactory because they leave too much room for potential abuses of ML decision-making. We conclude that both ethical and legal solutions are fraught with serious difficulties. These difficulties prompt us to re-weigh the costs and benefits of using ML for high-stake decisions. (shrink)

The notion of trustworthy AI has been proposed in response to mounting public criticism of AI systems, in particular with regard to the proliferation of such systems into ever more sensitive areas of human life without proper checks and balances. In Europe, the High-Level Expert Group on Artificial Intelligence has recently presented its Ethics Guidelines for Trustworthy AI. To some, the guidelines are an important step for the governance of AI. To others, the guidelines distract effort from genuine AI regulation. (...) In this chapter, we engage in a critical discussion of the concept of trustworthy AI by probing the concept both on theoretical and practical grounds, assessing its substance and the feasibility of its intent. We offer a concise overview of the guidelines and their vision for trustworthy AI and examine the conceptual underpinnings of trustworthy AI by considering how notions of 'trust' and 'trustworthiness' have been discussed in the philosophical literature. We then discuss several epistemic obstacles and moral requirements when striving to achieve trustworthy AI in practice before concluding with an argument in support of the establishment of a trustworthy AI culture that respects and protects foundational values. (shrink)

The research analyzes the process of formation of the ethics committee as a new institution in the system of regulation of genetic research. The external factors of this process are the increasing digitalization of medical and research practices, as well as the special situation that is developing in the field of genomic research and the use of genetic technologies, where issues of philosophy, jurisprudence and administration have generated many fundamentally new, and sometimes unexpected contexts. The author shows the similarity and (...) difference of approaches to the inclusion of the ethics committee in the national regulatory systems of different countries and determines the prospects for further integration of the new institution into normative and administrative-regulatory and law enforcement practices. The research applies all the main methods of system analysis and structural and functional analysis, as well as the comparative legal method. At the stage of generalization of empirical material, the author turns to the means and methods of philosophical phenomenology and phenomenological sociology. The study revealed that the ethics committee may have different institutional and regulatory functions in a variety of management and legal systems. Being hybrid in nature, the ethics committee performs various functions, and in various state legal systems and traditions it can combine elements of legislative, executive and judicial power. At times, the ethics committee may go beyond the scope of both a purely administrative and exclusively legal regulator. All this indicates that the integration of this institution into the legal system and into a variety of social practices is far from being completed. (shrink)

Many policies and ethical guidelines recommend developing “trustworthy AI”. We argue that developing morally trustworthy AI is not only unethical, as it promotes trust in an entity that cannot be trustworthy, but it is also unnecessary for optimal calibration. Instead, we show that reliability, exclusive of moral trust, entails the appropriate normative constraints that enable optimal calibration and mitigate the vulnerability that arises in high-stakes hybrid decision-making environments, without also demanding, as moral trust would, the anthropomorphization of (...) AI and thus epistemically dubious behavior. The normative demands of reliability for inter-agential action are argued to be met by an analogue to procedural metacognitive competence (i.e., the ability to evaluate the quality of one’s own informational states to regulate subsequent action). Drawing on recent empirical findings that suggest providing reliability scores (e.g., F1-scores) to human decision-makers improves calibration in the AI system, we argue that reliability scores provide a good index of competence and enable humans to determine how much they wish to rely on the system. (shrink)