Research map / Learning theory and algorithms / Fairness
LUCID: Exposing Algorithmic Bias through Inverse Design
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use canonical-set analysis to inspect protected-feature distributions | UCI Adult classifier · UCI Adult | protected-feature distribution | Mixed |
| Use canonical-set analysis to inspect protected-feature distributions | COMPAS classifier · COMPAS | protected-feature distribution | Mixed |
| Compare canonical-set analysis with output-based fairness metrics | UCI Adult classifier · UCI Adult | agreement on detected group preferences | Mixed |
| Compare canonical-set analysis with output-based fairness metrics | COMPAS classifier · COMPAS | agreement on detected group preferences | No clear effect |
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- Causal Bias Detection in Generative Artifical Intelligence (NeurIPS 2026 · similarity 0.87)
- Fairness-Accuracy Trade-Offs: A Causal Perspective (AAAI 2025 · similarity 0.86)
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