Knowledge-Enhanced Language Models Are Not Bias-Proof: Situated Knowledge and Epistemic Injustice in AI

Abstract

The factual inaccuracies ("hallucinations") of large language models have recently inspired more research on knowledge-enhanced language modeling approaches. These are often assumed to enhance the overall trustworthiness and objectivity of language models. Meanwhile, the issue of bias is usually only mentioned as a limitation of statistical representations. This dissociation of knowledge-enhancement and bias is in line with previous research on AI engineers’ assumptions about knowledge, which indicate that knowledge is commonly understood as objective and value-neutral by this community. We argue that claims and practices by actors of the field still reflect this underlying conception of knowledge. We contrast this assumption with literature from social and, in particular, feminist epistemology, which argues that the idea of a universal disembodied knower is blind to the reality of knowledge practices and seriously challenges claims of "objective" or "neutral" knowledge.

Ähnliche Arbeiten