FairStress: A deeper evaluation of fairness in LLM decision-making

Ai2’s FairStress benchmark evaluates demographic fairness in large language model decision-making beyond final choices by testing whether models apply evidence consistently, resist matched persuasive challenges equally, and explain decisions similarly across demographic groups. It comprises 300 two-candidate scenarios spanning 29 high-stakes domains, with variations in identity cues and adversarial prefixes producing more than 13 million cases, alongside a smaller 50,000-item FairStress-Core subset. Tests across open and frontier models found that behavior was largely symmetric when no demographic information was provided but became less so when identities were implied or explicit, with models often more readily abandoning evidence-supported choices when doing so favored minority-coded candidates, a pattern the authors call anti-stereotypical over-correction. The study also reports differences in explanation language and limited evidence that internal activations encode demographic selection patterns not fully reflected in reasoning traces. Comparisons with the BBQ bias benchmark showed that measured bias direction can change substantially depending on prompting, chat formatting, and scoring methods. Audits of Olmo 3 training data identified limited merit-based decision examples and unequal demographic framing, though causation remains unresolved, while tests of nine debiasing methods found that most did not reliably improve symmetry and some reduced accuracy; counterfactual chain-of-thought showed comparatively promising results.

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