Dancing in the Shadows: Harnessing Ambiguity for Fairer Classifiers
<|reference_start|>Dancing in the Shadows: Harnessing Ambiguity for Fairer Classifiers: 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.<|reference_end|>
@article{barrainkua2024dancing,
    title={Dancing in the Shadows: Harnessing Ambiguity for Fairer Classifiers},
    author={Ainhize Barrainkua, Paula Gordaliza, Jose A. Lozano, Novi Quadrianto},
    journal={Presented at the XI Symposium of Theory and Applications of Data
  Mining from the XX Conference of the Spanish Association for Artificial
  Intelligence CAEPIA 2024},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.19066},
    primaryClass={cs.LG cs.CY}
}