New framework formalizes causal fairness for continuous protected attributes via path-specific derivatives and introduces a tuning algorithm for fair predictors.
Path-Specific Counterfactual Fairness
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LLMs produce lower-fidelity summaries of identical public comments when attributed to lower-status occupations like street vendors versus financial analysts, with inconsistent race effects and no gender effects.
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Tuning Derivatives for Causal Fairness in Machine Learning
New framework formalizes causal fairness for continuous protected attributes via path-specific derivatives and introduces a tuning algorithm for fair predictors.
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All Public Voices Are Equal, But Are Some More Equal Than Others to LLMs?
LLMs produce lower-fidelity summaries of identical public comments when attributed to lower-status occupations like street vendors versus financial analysts, with inconsistent race effects and no gender effects.