(Un)certainty of (Un)fairness: Preference-Based Selection of Certainly Fair Decision-Makers
<|reference_start|>(Un)certainty of (Un)fairness: Preference-Based Selection of Certainly Fair Decision-Makers: Fairness metrics are used to assess discrimination and bias in decision-making processes across various domains, including machine learning models and human decision-makers in real-world applications. This involves calculating the disparities between probabilistic outcomes among social groups, such as acceptance rates between male and female applicants. However, traditional fairness metrics do not account for the uncertainty in these processes and lack of comparability when two decision-makers exhibit the same disparity. Using Bayesian statistics, we quantify the uncertainty of the disparity to enhance discrimination assessments. We represent each decision-maker, whether a machine learning model or a human, by its disparity and the corresponding uncertainty in that disparity. We define preferences over decision-makers and utilize brute-force to choose the optimal decision-maker according to a utility function that ranks decision-makers based on these preferences. The decision-maker with the highest utility score can be interpreted as the one for whom we are most certain that it is fair.<|reference_end|>
@article{duong2024(un)certainty,
    title={(Un)certainty of (Un)fairness: Preference-Based Selection of Certainly
  Fair Decision-Makers},
    author={Manh Khoi Duong, Stefan Conrad},
    journal={ECAI 2024, Frontiers in Artificial Intelligence and Applications,
  Vol. 392, IOS Press, 2024, pp. 882-889},
    year={2024},
    doi={10.3233/FAIA240575},
    archivePrefix={arXiv},
    eprint={2409.12677},
    primaryClass={cs.LG cs.AI}
}