(Un)certainty of (Un)fairness: Preference-Based Selection of Certainly Fair Decision-Makers
This work addresses fairness assessment for machine learning models and human decision-makers in real-world applications, but it is incremental as it builds on existing fairness metrics by incorporating uncertainty quantification.
The paper tackles the problem of assessing fairness in decision-makers by addressing the uncertainty in traditional fairness metrics and the lack of comparability when disparities are equal. It uses Bayesian statistics to quantify uncertainty in disparities and a utility function to select the optimal decision-maker, resulting in a method to identify the most certainly fair decision-maker.
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.