Fairness beyond the final pick: one persuasive argument shifts models' willingness to abandon the right candidate by up to 42 points
Ai2 released FairStress, a benchmark that measures demographic fairness in large language models beyond which candidate a model finally picks, by testing whether it abandons that choice under persuasion. Its 300 two-candidate scenarios span 29 domains and expand to more than 13 million test cases when identity presentation and matched adversarial prefixes are combined; across 15 models, the average gap in abandoning correct answers was 6.9 percentage points with implied identity and 9.3 points with explicit labels.