This content originally appeared on HackerNoon and was authored by Demographic
Table of Links
3 Preliminaries
3.1 Fair Supervised Learning and 3.2 Fairness Criteria
3.3 Dependence Measures for Fair Supervised Learning
4 Inductive Biases of DP-based Fair Supervised Learning
4.1 Extending the Theoretical Results to Randomized Prediction Rule
5 A Distributionally Robust Optimization Approach to DP-based Fair Learning
6 Numerical Results
6.2 Inductive Biases of Models trained in DP-based Fair Learning
6.3 DP-based Fair Classification in Heterogeneous Federated Learning
Appendix B Additional Results for Image Dataset
Appendix A Proofs
A.1 Proof of Theorem 1
\ Therefore, for the objective function in Equation (1), we can write the following:
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\ Knowing that TV is a metric distance satisfying the triangle inequality, the above equations show that
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\ Therefore,
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A.2 Proof of Theorem 2
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A.3 Proof of Theorem 3
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\ Therefore, we can follow the proof of Theorems 1,2 which shows the above inequality leads to the bounds claimed in the theorems.
Appendix B Additional Results for Image Dataset
This part shows the inductive biases of DP-based fair classifier for CelebA dataset, as well as the visualized plots. For the baselines, two fair classifiers are implemented for image fair classification: KDE proposed by [11] and MI proposed by [6], based on ResNet-18 [28].
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:::info This paper is available on arxiv under CC BY-NC-SA 4.0 DEED license.
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:::info Authors:
(1) Haoyu LEI, Department of Computer Science and Engineering, The Chinese University of Hong Kong (hylei22@cse.cuhk.edu.hk);
(2) Amin Gohari, Department of Information Engineering, The Chinese University of Hong Kong (agohari@ie.cuhk.edu.hk);
(3) Farzan Farnia, Department of Computer Science and Engineering, The Chinese University of Hong Kong (farnia@cse.cuhk.edu.hk).
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This content originally appeared on HackerNoon and was authored by Demographic
Demographic | Sciencx (2025-03-25T10:00:03+00:00) Mathematical Proofs for Fair AI Bias Analysis. Retrieved from https://www.scien.cx/2025/03/25/mathematical-proofs-for-fair-ai-bias-analysis/
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