Kant begins his Lectures on Pedagogy by stating, “[t]he human being is the only creature that must be educated” (Kant, 2007, 9:441), and he argues that it is through education that we can transform our initial “animal nature into human nature” (ibid. 2007, 9:441). Kant understands education as involving an ordered process of care, discipline, instruction and formation through enculturating, civilizing and moralizing (Formosa 2011). Further, Kant envisages that we should pursue as a species the “moral perfection” that is the (...) “final destiny of the human race” through education (Collins, 1997, 27:470; see Dean, 2014). However, to engage in this pursuit Kant believes that, through education and social change, we have to regulate our “animal nature” and counter the moral corruption of our species, which he calls the “radical innate evil in human nature (not any the less brought upon us by ourselves)” (Kant, 1998, 6:32). If humanity is to pursue its final destiny of moral perfection, then education will need to respond responsibly to the propensity to evil that is deeply rooted in us as finite and imperfect rational beings living in imperfect and at times even in morally corrupted social conditions. This paper outlines some of the relevant issues. (shrink)

As robots and Artificial Intelligences become more enmeshed in rich social contexts, it seems inevitable that we will have to make them into moral machines equipped with moral skills. Apart from the technical difficulties of how we could achieve this goal, we can also ask the ethical question of whether we should seek to create such Artificial Moral Agents (AMAs). Recently, several papers have argued that we have strong reasons not to develop AMAs. In response, we develop a comprehensive analysis (...) of the relevant arguments for and against creating AMAs, and we argue that all things considered we have strong reasons to continue to responsibly develop AMAs. The key contributions of this paper are threefold. First, to provide the first comprehensive response to the important arguments made against AMAs by Wynsberghe and Robbins (in “Critiquing the Reasons for Making Artificial Moral Agents”, Science and Engineering Ethics 25, 2019) and to introduce several novel lines of argument in the process. Second, to collate and thematise for the first time the key arguments for and against AMAs in a single paper. Third, to recast the debate away from blanket arguments for or against AMAs in general, to a more nuanced discussion about the use of what sort of AMAs, in what sort of contexts, and for what sort of purposes is morally appropriate. (shrink)

For Kant, we cannot understand how to approach moral education without confronting the radical evil of humanity. But if we start out, as Kant thinks we do, from a morally corrupt state, how...

People give surprising weight to others’ expectations about their behaviour. I argue the practice of conforming to others’ expectations is ethically well-grounded. A special class of ‘reasonable expectations’ can create prima facie obligations even in cases where the expectations arise from contingent pre-existing practices, and the duty-bearer has not created them, or directly benefited from them. The obligation arises because of the substantial goods that follow from such conformity—goods capable of being endorsed from many different ethical perspectives and implicating key (...) moral factors such as consent, fairness, respect, autonomy, and reciprocity. Given the innumerable situations where such expectations can arise, their ethical significance is critical both practically and philosophically. (shrink)

“Meta-argument allegations” consist of protestations that an interlocutor’s speech is wrongfully offensive or will trigger undesirable social consequences. Such protestations are meta-argument in the sense that they do not interrogate the soundness of an opponent’s argumentation, but instead focus on external features of that argument. They are allegations because they imply moral wrongdoing. There is a legitimate place for meta-argument allegations, and the moral and epistemic goods that can come from them will be front of mind for those levelling such (...) allegations. But I argue there is a dark side to such allegations, and their epistemic and moral costs must be seriously weighed. Meta-argument allegations have a concerning capacity to derail discussions about important topics, stymieing argumentational interactions and the goods they provide. Such allegations can license efforts to silence, punish and deter—even as they provoke the original speaker to retaliate in kind. Used liberally, such allegations can escalate conflicts, block open-mindedness, and discourage constructive dialogues. In response, I defend “argumentational tolerance”—a principled wariness in employing meta-argument allegations—as a virtue of ethical argument. (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)