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)

This paper introduces the concept of Operator Contestability in AI systems: the principle that those overseeing AI systems (operators) must have the necessary control to be accountable for the decisions made by these algorithms. We argue that designers have a duty to ensure operator contestability. We demonstrate how this duty can be fulfilled by applying the'Design for Defeaters' framework, which provides strategies to embed tools within AI systems that enable operators to challenge decisions. Defeaters are designed to contest either the (...) justification for the AI’s data inputs (undercutting defeaters) or the validity of the conclusions drawn from that data (rebutting defeaters). To illustrate the necessity and application of this framework, we examine case studies such as AI-driven recruitment processes, where operators need tools and authority to uncover and address potential biases, and autonomous driving systems, where real-time decision-making is crucial. The paper argues that operator contestability requires ensuring that operators have (1) epistemic access to the relevant normative reasons and (2) the authority and cognitive capacity to act on these defeaters. By addressing these challenges, the paper emphasizes the importance of designing AI systems in a way that enables operators to effectively contest AI decisions, thereby ensuring that the appropriate individuals can take responsibility for the outcomes of human-AI interactions. (shrink)

Whatever one makes of the relationship between free will and moral responsibility – e.g. whether it’s the case that we can have the latter without the former and, if so, what conditions must be met; whatever one thinks about whether artificially intelligent agents might ever meet such conditions, one still faces the following questions. What is the value of moral responsibility? If we take moral responsibility to be a matter of being a fitting target of moral blame or praise, what (...) are the goods attached to them? The debate concerning ‘machine morality’ is often hinged on whether artificial agents are or could ever be morally responsible, and it is generally taken for granted (following Matthias 2004) that if they cannot, they pose a threat to the moral responsibility system and associated goods. In this paper, I challenge this assumption by asking what the goods of this system, if any, are, and what happens to them in the face of artificially intelligent agents. I will argue that they neither introduce new problems for the moral responsibility system nor do they threaten what we really (ought to) care about. I conclude the paper with a proposal for how to secure this objective. (shrink)

Complex technologies such as Artificial Intelligence (AI) can cause harm, raising the question of who is liable for the harm caused. Research has identified multiple liability gaps (i.e., unsatisfactory outcomes when applying existing liability rules) in legal frameworks. In this paper, the concepts of shared responsibilities and fiduciary duties are explored as avenues to address liability gaps. The development, deployment and use of complex technologies are not clearly distinguishable stages, as often suggested, but are processes of cooperation and co-creation. At (...) the intersections of these stages, shared responsibilities and fiduciary duties of multiple actors can be observed. Although none of the actors have complete control or a complete overview, many actors have some control or influence, and, therefore, responsibilities based on fault, prevention or benefit. Shared responsibilities and fiduciary duties can turn liability gaps into liability overlaps. These concepts could be implemented in tort and contract law by amending existing law (e.g., by assuming that all stakeholders are liable unless they can prove they did not owe a duty of care) and by creating more room for partial liability reflecting partial responsibilities (e.g., a responsibility to signal or identify an issue without a corresponding responsibility to solve that issue). This approach better aligns legal liabilities with responsibilities, increases legal certainty, and increases cooperation and understanding between actors, improving the quality and safety of technologies. However, it may not solve all liability gaps, may have chilling effects on innovation, and may require further detailing through case law. (shrink)

This paper concerns the double standard debate in the ethics of AI literature. This debate essentially revolves around the question of whether we should subject AI systems to different normative standards than humans. So far, the debate has centered around the desideratum of transparency. That is, the debate has focused on whether AI systems must be more transparent than humans in their decision-making processes in order for it to be morally permissible to use such systems. Some have argued that the (...) same standards of transparency should be applied across the board, for AI systems and humans alike. Others have argued that we should hold AI systems to higher standards than humans in terms of transparency. In this paper, we first point out that there are structurally similar double standard debates to be had about other desiderata besides transparency, such as predictive accuracy. Second, we argue that when we focus on predictive accuracy, there are at least two reasons for holding AI systems to a lower standard than humans. (shrink)