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)

This article tries to shed light on the difference between human autonomy and AI-driven machine autonomy. The breadth of the studies concerning this topic is constantly increasing, and for this reason, this discussion is very narrow and limited in its extent. Indeed, its hypothesis is that it is possible to distinguish two kinds of autonomy by analysing the way humans and robots narrate stories and the types of stories that, respectively, result from their capability of narrating stories on their own. (...) To approach the question of autonomy and its different interpretations in robots and humans, using narratives and stories as a lens, I proceed through four steps. First, I provide a brief summary concerning the state of the art with regard to issues concerning autonomy and AI. Second, I focus on specific issues concerning AI that are applied to storytelling. Third, I progressively approach narrative autonomy through what could seem a useless digression by addressing the problem of human creativity in relation to machine creativity. Fourth, I hypothesize a radical difference between narrative autonomy—which is proper to humans—and storytelling capability, which can be observed in both humans and machines. (shrink)

Law and the legal system through which law is effected are very powerful, yet the power of the law has always been limited by the laws of nature, upon which the law has now direct grip. Human law now faces an unprecedented challenge, the emergence of a second limit on its grip, a new “species” of intelligent agents (AI machines) that can perform cognitive tasks that until recently only humans could. What happens, as a matter of law, when another species (...) interacts with us, can be integrated into human minds and bodies, makes “real-world” decisions—not through human proxies, but directly—and does all this “intelligently”, with what one could call autonomous agency or even a “mind” of its own? The article starts from the clear premise that control cannot be exercised directly on AI machines through human law. That control can only be effected through laws that apply to humans. This has several regulatory implications. The article’s first discusses what, in any attempt to regulate AI machines, the law can achieve. Having identified what the law can do, the article then canvases what the law should aim to achieve overall. The article encapsulate its analysis in a list of both doctrinal and normative principles that should underpin any regulation aimed at AI machines. Finally, the article compares three transnational options to implement the proposed regulatory approach. (shrink)

In this philosophical exploration, we investigate the concept of robotic moral environment interaction. The common view understands moral interaction to occur between agents endowed with ethical and interactive capacities. However, recent developments in moral philosophy argue that moral interaction also occurs in relation to the environment. Here conditions and situations of the environment contribute to human moral cognition and the formation of our moral experiences. Based on this philosophical position, we imagine robots interacting as moral environments—a novel conceptualization of human–robot (...) moral interaction with an inherent capacity for moral augmentation. To explicate this idea, we first define moral environments as moral systems providing moral affordances. We then intuit and explicate two constitutive conditions of moral environments: the environment’s moral ambiance and its moral atmosphere and compare them with real-life cases of moral environments—the moral landmarks. Based on these explications, we construct several explanatory cases to illustrate robots interacting as moral environments. Lastly, we set to evaluate the ethical challenges and wider social ramifications of using moral environment robots in the public space. (shrink)

Social robots are robots that can interact socially with humans. As social robots and the artificial intelligence that powers them becomes more advanced, they will likely take on more social and work roles. This has many important ethical implications. In this paper, we focus on one of the most central of these, the impacts that social robots can have on human autonomy. We argue that, due to their physical presence and social capacities, there is a strong potential for social robots (...) to enhance human autonomy as well as several ways they can inhibit and disrespect it. We argue that social robots could improve human autonomy by helping us to achieve more valuable ends, make more authentic choices, and improve our autonomy competencies. We also argue that social robots have the potential to harm human autonomy by instead leading us to achieve fewer valuable ends ourselves, make less authentic choices, decrease our autonomy competencies, make our autonomy more vulnerable, and disrespect our autonomy. Whether the impacts of social robots on human autonomy are positive or negative overall will depend on the design, regulation, and use we make of social robots in the future. (shrink)

The increasing workplace use of artificially intelligent (AI) technologies has implications for the experience of meaningful human work. Meaningful work refers to the perception that one’s work has worth, significance, or a higher purpose. The development and organisational deployment of AI is accelerating, but the ways in which this will support or diminish opportunities for meaningful work and the ethical implications of these changes remain under-explored. This conceptual paper is positioned at the intersection of the meaningful work and ethical AI (...) literatures and offers a detailed assessment of the ways in which the deployment of AI can enhance or diminish employees’ experiences of meaningful work. We first outline the nature of meaningful work and draw on philosophical and business ethics accounts to establish its ethical importance. We then explore the impacts of three paths of AI deployment (replacing some tasks, ‘tending the machine’, and amplifying human skills) across five dimensions constituting a holistic account of meaningful work, and finally assess the ethical implications. In doing so we help to contextualise the meaningful work literature for the era of AI, extend the ethical AI literature into the workplace, and conclude with a range of practical implications and future research directions. (shrink)

Artificial intelligence is increasingly inputting into various human resource management functions, such as sourcing job applicants and selecting staff, allocating work, and offering personalized career coaching. While the use of AI for such tasks can offer many benefits, evidence suggests that without careful and deliberate implementation its use also has the potential to generate significant harms. This raises several ethical concerns regarding the appropriateness of AI deployment to domains such as HRM, which directly deal with managing sometimes sensitive aspects of (...) individuals’ employment lifecycles. However, research at the intersection of HRM and technology continues to largely center on examining what AI can be used for, rather than focusing on the salient factors relevant to its ethical use and examining how to effectively engage human workers in its use. Conversely, the ethical AI literature offers excellent guiding principles for AI implementation broadly, but there remains much scope to explore how these principles can be enacted in specific contexts-of-use. By drawing on ethical AI and task-technology fit literature, this paper constructs a decision-making framework to support the ethical deployment of AI for HRM and guide determinations of the optimal mix of human and machine involvement for different HRM tasks. Doing so supports the deployment of AI for the betterment of work and workers and generates both scholarly and practical outcomes. (shrink)

Forms of Artificial Intelligence (AI) are already being deployed into clinical settings and research into its future healthcare uses is accelerating. Despite this trajectory, more research is needed regarding the impacts on patients of increasing AI decision making. In particular, the impersonal nature of AI means that its deployment in highly sensitive contexts-of-use, such as in healthcare, raises issues associated with patients’ perceptions of (un) dignified treatment. We explore this issue through an experimental vignette study comparing individuals’ perceptions of being (...) treated in a dignified and respectful way in various healthcare decision contexts. Participants were subject to a 2 (human or AI decision maker) x 2 (positive or negative decision outcome) x 2 (diagnostic or resource allocation healthcare scenario) factorial design. We found evidence of a “human bias” (i.e., a preference for human over AI decision makers) and an “outcome bias” (i.e., a preference for positive over negative outcomes). However, we found that for perceptions of respectful and dignified interpersonal treatment, it matters more who makes the decisions in diagnostic cases and it matters more what the outcomes are for resource allocation cases. We also found that humans were consistently viewed as appropriate decision makers and AI was viewed as dehumanizing, and that participants perceived they were treated better when subject to diagnostic as opposed to resource allocation decisions. Thematic coding of open-ended text responses supported these results. We also outline the theoretical and practical implications of these findings. (shrink)

Digital me ontology and ethics. 21 December 2020. -/- Ljupco Kocarev and Jasna Koteska. -/- This paper addresses ontology and ethics of an AI agent called digital me. We define digital me as autonomous, decision-making, and learning agent, representing an individual and having practically immortal own life. It is assumed that digital me is equipped with the big-five personality model, ensuring that it provides a model of some aspects of a strong AI: consciousness, free will, and intentionality. As computer-based personality (...) judgments are more accurate than those made by humans, digital me can judge the personality of the individual represented by the digital me, other individuals’ personalities, and other digital me-s. We describe seven ontological qualities of digital me: a) double-layer status of Digital Being versus digital me, b) digital me versus real me, c) mind-digital me and body-digital me, d) digital me versus doppelganger (shadow digital me), e) non-human time concept, f) social quality, g) practical immortality. We argue that with the advancement of AI’s sciences and technologies, there exist two digital me thresholds. The first threshold defines digital me having some (rudimentarily) form of consciousness, free will, and intentionality. The second threshold assumes that digital me is equipped with moral learning capabilities, implying that, in principle, digital me could develop their own ethics which significantly differs from human’s understanding of ethics. Finally we discuss the implications of digital me metaethics, normative and applied ethics, the implementation of the Golden Rule in digital me-s, and we suggest two sets of normative principles for digital me: consequentialist and duty based digital me principles. -/- Authors are ordered alphabetically and equally contributed to the paper. (shrink)