A report by the Center for Democracy and Technology looks at teachers' and students' experiences with the technology.

Advances in artificial intelligence offer an enticing solution to a global problem: perhaps interacting with large language models (LLMs) can help all…

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Lawyers blame IT, family emergencies, their own poor judgment, their assistants, illness, and more.

This paper contends with the notion that the methods of machine learning (ML) are unique among the tools of science in enabling a form of theory-free inductive inference. I challenge these assertions of epistemic distinctness, attributing the prevalence of these views to an untenable conception of scientific objectivity: what I term a theory-free ideal, in homage to its normative counterpart. ML, as a formal method of induction, must rely on conceptual or theoretical resources to get inference off the ground. By means of two case studies, I argue that this theory-free ideal has a deleterious effect on the epistemic standing of ML-involving science.

Large language models (LLMs) are playing an increasingly integral, though largely informal, role in scholarly peer review. Yet it remains unclear whether LLMs reproduce the biases observed in human de...

LLM “tended to sacrifice accuracy for simplicity” when writing news briefs.