Dancing in the Shadows: Harnessing Ambiguity for Fairer Classifiers
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Abstract (arXiv)
This paper introduces a novel approach to bolster algorithmic fairness in scenarios where sensitive information is only partially known. In particular, we propose to leverage instances with uncertain identity with regards to the sensitive attribute to train a conventional machine learning classifier. The enhanced fairness observed in the final predictions of this classifier highlights the promising potential of prioritizing ambiguity (i.e., non-normativity) as a means to improve fairness guarantees in real-world classification tasks.
Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 13 of its 20 references that are in the map. First posted on arXiv 27 Jun 2024.
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References · 13
- 2016Equality of Opportunity in Supervised Learningcited by 2,000
- 2017Avoiding Discrimination through Causal Reasoningcited by 191
- 2022Correct-N-Contrast: A Contrastive Approach for Improving Robustness to Spurious Correlationscited by 171
- 2020Algorithmic Fairnesscited by 143
- 2017Runaway Feedback Loops in Predictive Policingcited by 97
- 2022Spread Spurious Attribute: Improving Worst-group Accuracy with Spurious Attribute Estimationcited by 88
- 2014Automated Experiments on Ad Privacy Settings: A Tale of Opacity, Choice, and Discriminationcited by 83
- 2019Assessing Algorithmic Fairness with Unobserved Protected Class Using Data Combinationcited by 79
- 2020A survey of bias in Machine Learning through the prism of Statistical Parity for the Adult Data Setcited by 35
- 2021Evaluating Fairness of Machine Learning Models Under Uncertain and Incomplete Informationcited by 33
- 2021Multiaccurate Proxies for Downstream Fairnesscited by 17
- 2022Towards Group Robustness in the presence of Partial Group Labelscited by 6
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