T.J. Broy

The ethics of medical training are complex, balancing the need to provide trainees with experience while minimizing the potential harm to patients from their lack of skill. Our current tolerance for preventable medical errors is appropriately low, yet our human nature makes the elimination of all error impossible. Advances in medical simulation have provided new ways of training that reduce risk to patients. Simulation provides a unique opportunity to allow trainees to learn specific psychomotor skills, work through complicated patient-care scenarios, improve their teamwork, and learn from mistakes without affecting direct patient care. In this chapter, the authors examine available simulation modalities such as low-fidelity skill trainers, virtual reality trainers, high-fidelity manikins, and standardized patients that can be employed to provide medical education in an ethical manner. The authors then provide an analysis of the ethical costs and benefits of employing simulations in medical education.

AI-critical artists have frequently made the claim that ‘AI art is theft’. Goetze (2024) and Shoemaker (2024) have considered how best to interpret the claim that AI art is theft and whether such a claim can be substantiated. This article aims to reorient the discussion away from considering the ways in which AI might be a form of theft (I argue it is not) and instead towards considering what features of AI art as understood in its own right might motivate such a complaint—that is, this article aims to move away from interpreting what artists mean when they say ‘AI art is theft’ and towards considering why they might be saying it. I conclude that instead of being a form of theft, AI art involves violations of norms internal to artistic practice. Whether such norms might evolve to incorporate AI art is a contingent historical matter, not one suited to being settled by a priori reflection.

Automated vehicles promise much in the way of both economic boons and increased personal safety. For better or worse, the effects of automating personal vehicles will not be felt for some time. In contrast, the effects of automated work vehicles, like semi-trucks, will be felt much sooner—within the next decade. The costs and benefits of automation will not be distributed evenly; while most of us will be positively affected by the lower prices overall, those losing their livelihoods to the automated semi-trucks and other similar work vehicles will be much worse off. This sets up a classic distributive justice problem: how do we balance the harms and benefits of automation? In this paper, the authors recommend work alternatives: policies that the government would enact in order to ensure that each person could live a decent life even if one could not work, especially for reasons like having one’s job automated. After arguing for this position, we propose some policy guidelines.