What question did this study set out to answer?

To develop a practical machine learning framework that guarantees individual fairness using path-specific causal effects without relying on restrictive data assumptions.

February 17, 2020Open Access

Learning Individually Fair Classifier with Path-Specific Causal-Effect Constraint

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Authors

YCYoichi Chikahara SSShinsaku Sakaue AFAkinori Fujino

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Overview

Computational analysis demonstrates guaranteed individual algorithmic fairness under path-specific causal constraints, suggesting practical bias reduction with minimal accuracy loss.

Key Points

To develop a practical machine learning framework that guarantees individual fairness using path-specific causal effects without relying on restrictive data assumptions.
Formulated a new criterion termed the probability of individual unfairness (PIU) to measure individual-level bias along specific causal pathways.
Constructed a constrained optimization problem that minimizes an empirical upper bound of PIU estimated directly from observational data.
Provided theoretical guarantees ensuring individual fairness without imposing impractical functional form assumptions on the underlying data distribution.
Demonstrated experimentally that the algorithm reliably enforces fairness for each individual while incurring only a minor reduction in classification accuracy.

Cite This Study

Chikahara et al. (2020) studied this question.

synapsesocial.com/papers/6a128ebec031bb6829a6de43 https://doi.org/10.48550/arxiv.2002.06746

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February 17, 2020Open Access

Learning Individually Fair Classifier with Path-Specific Causal-Effect Constraint

View Full Paper

Ask AI

Bookmark

Authors

YCYoichi Chikahara SSShinsaku Sakaue AFAkinori Fujino

Discussion

Member takes

Overview

Computational analysis demonstrates guaranteed individual algorithmic fairness under path-specific causal constraints, suggesting practical bias reduction with minimal accuracy loss.

Key Points

To develop a practical machine learning framework that guarantees individual fairness using path-specific causal effects without relying on restrictive data assumptions.
Formulated a new criterion termed the probability of individual unfairness (PIU) to measure individual-level bias along specific causal pathways.
Constructed a constrained optimization problem that minimizes an empirical upper bound of PIU estimated directly from observational data.
Provided theoretical guarantees ensuring individual fairness without imposing impractical functional form assumptions on the underlying data distribution.
Demonstrated experimentally that the algorithm reliably enforces fairness for each individual while incurring only a minor reduction in classification accuracy.

Cite This Study

Chikahara et al. (2020) studied this question.

synapsesocial.com/papers/6a128ebec031bb6829a6de43 https://doi.org/10.48550/arxiv.2002.06746

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