In the wake of ChatGPT’s release, academics and journal editors have begun making important decisions about whether and how to integrate generative artificial intelligence (AI) into academic publishing. Some argue that AI outputs in scholarly works constitute plagiarism, and so should be disallowed by academic journals. Others suggest that it is acceptable to integrate AI output into academic papers, provided that its contributions are transparently disclosed. By drawing on Taylor’s work on academic norms, this paper argues against both views. Unlike (...) “traditional” forms of plagiarism, use of generative AI can be consistent with the norms that should underlie academic research. In these cases, its use should neither be prohibited nor required to be disclosed. However, some careless uses of generative AI do threaten to undermine the quality of academic research by mischaracterizing existing literature. This, not “AI plagiarism,” is the real concern raised by ChatGPT and related technologies. (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)