Proceedings

Towards Fairness with Limited Demographics via Disentangled Learning

Zichong Wang, Anqi Wu, Nuno Moniz, Shu Hu, Bart Knijnenburg, Xingquan Zhu, Wenbin Zhang

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Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence. Main Track. Pages 565-573.

https://doi.org/10.24963/ijcai.2025/64

Keywords

  • AI Ethics, Trust, Fairness: ETF: Fairness and diversity
  • AI Ethics, Trust, Fairness: ETF: Trustworthy AI

Abstract

Fairness in artificial intelligence has garnered increasing attention due to concerns about discriminatory AI-based decision-making, prompting the development of numerous mitigation approaches. However, most existing methods assume that demographic information is readily available, which may not align with real-world scenarios where such information is often incomplete. To this end, this paper tackles the pervasive yet overlooked challenge of developing fair machine learning algorithms with limited demographics. Specifically, we explore leveraging limited demographic information to accurately infer missing demographics while simultaneously evaluating and optimizing model fairness. We argue that this approach better aligns with common real-world socially sensitive scenarios involving limited demographics. Extensive experiments on three benchmark datasets highlight the effectiveness of the proposed method, surpassing state-of-the-art with significant gains in fairness while maintaining comparable utility.

Zichong Wang, Anqi Wu, Nuno Moniz, Shu Hu, Bart Knijnenburg, Xingquan Zhu, and Wenbin Zhang. 2025. Towards Fairness with Limited Demographics via Disentangled Learning. In Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence, IJCAI-25. IJCAI, 565–573. https://doi.org/10.24963/ijcai.2025/64
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