Research map / Learning theory and algorithms / Fairness
Beyond Distributive Fairness in Algorithmic Decision Making: Feature Selection for Procedurally Fair Learning
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| constrained submodular optimization (ISK) instead of brute-force feature subset search | accuracy maximization under process-unfairness constraint · ProPublica COMPAS, NYPD SQF | accuracy | No clear effect |
| impose high process (feature-apriori) fairness constraint | logistic regression feature selection · ProPublica COMPAS, NYPD SQF | accuracy | Hurt |
| impose high process fairness | logistic regression feature selection · ProPublica COMPAS, NYPD SQF | outcome fairness (disparate mistreatment) | Helped |
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