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Home / Proceedings / Proceedings of the AAAI Conference on Artificial Intelligence

Fairness for Robust Log Loss Classification

February 1, 2023

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Authors

Ashkan Rezaei

UIC


Rizal Fathony

CMU


Omid Memarrast

UIC


Brian Ziebart

UIC


DOI:

10.1609/aaai.v34i04.6002


Abstract:

Developing classification methods with high accuracy that also avoid unfair treatment of different groups has become increasingly important for data-driven decision making in social applications. Many existing methods enforce fairness constraints on a selected classifier (e.g., logistic regression) by directly forming constrained optimizations. We instead re-derive a new classifier from the first principles of distributional robustness that incorporates fairness criteria into a worst-case logarithmic loss minimization. This construction takes the form of a minimax game and produces a parametric exponential family conditional distribution that resembles truncated logistic regression. We present the theoretical benefits of our approach in terms of its convexity and asymptotic convergence. We then demonstrate the practical advantages of our approach on three benchmark fairness datasets.

Topics: AAAI

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HOW TO CITE:

Ashkan Rezaei||Rizal Fathony||Omid Memarrast||Brian Ziebart Fairness for Robust Log Loss Classification Proceedings of the AAAI Conference on Artificial Intelligence (2020) 5511-5518.

Ashkan Rezaei||Rizal Fathony||Omid Memarrast||Brian Ziebart Fairness for Robust Log Loss Classification AAAI 2020, 5511-5518.

Ashkan Rezaei||Rizal Fathony||Omid Memarrast||Brian Ziebart (2020). Fairness for Robust Log Loss Classification. Proceedings of the AAAI Conference on Artificial Intelligence, 5511-5518.

Ashkan Rezaei||Rizal Fathony||Omid Memarrast||Brian Ziebart. Fairness for Robust Log Loss Classification. Proceedings of the AAAI Conference on Artificial Intelligence 2020 p.5511-5518.

Ashkan Rezaei||Rizal Fathony||Omid Memarrast||Brian Ziebart. 2020. Fairness for Robust Log Loss Classification. "Proceedings of the AAAI Conference on Artificial Intelligence". 5511-5518.

Ashkan Rezaei||Rizal Fathony||Omid Memarrast||Brian Ziebart. (2020) "Fairness for Robust Log Loss Classification", Proceedings of the AAAI Conference on Artificial Intelligence, p.5511-5518

Ashkan Rezaei||Rizal Fathony||Omid Memarrast||Brian Ziebart, "Fairness for Robust Log Loss Classification", AAAI, p.5511-5518, 2020.

Ashkan Rezaei||Rizal Fathony||Omid Memarrast||Brian Ziebart. "Fairness for Robust Log Loss Classification". Proceedings of the AAAI Conference on Artificial Intelligence, 2020, p.5511-5518.

Ashkan Rezaei||Rizal Fathony||Omid Memarrast||Brian Ziebart. "Fairness for Robust Log Loss Classification". Proceedings of the AAAI Conference on Artificial Intelligence, (2020): 5511-5518.

Ashkan Rezaei||Rizal Fathony||Omid Memarrast||Brian Ziebart. Fairness for Robust Log Loss Classification. AAAI[Internet]. 2020[cited 2023]; 5511-5518.


ISSN: 2374-3468


Published by AAAI Press, Palo Alto, California USA
Copyright 2022, Association for the Advancement of
Artificial Intelligence 1900 Embarcadero Road, Suite
101, Palo Alto, California 94303 All Rights Reserved

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