Research map / Learning theory and algorithms / Prediction and decision losses
Optimized Pre-Processing for Discrimination Prevention
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Apply the optimized pre-processing transformation | COMPAS recidivism data · COMPAS recidivism data | KL divergence: 0.021 | Helped |
| Apply the optimized pre-processing transformation | UCI Adult · UCI Adult | L1 distance: 0.014 | Helped |
| Apply the optimized pre-processing transformation | COMPAS recidivism data · COMPAS recidivism, African-American males | recidivism rate: 0.593 -> 0.404 | Mixed |
| Apply the optimized pre-processing transformation | COMPAS recidivism data · COMPAS recidivism, Caucasian males | recidivism rate: 0.430 -> 0.404 | Mixed |
| Apply the optimized pre-processing transformation | COMPAS recidivism data · COMPAS recidivism, African-American females | recidivism rate: 0.393 -> 0.393 | No clear effect |
| Apply the optimized pre-processing transformation | COMPAS recidivism data · COMPAS recidivism, Caucasian females | recidivism rate: 0.367 -> 0.367 | No clear effect |
| Apply the optimized pre-processing transformation | UCI Adult · UCI Adult income rates by demographic group | high-income percentage | Mixed |
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