Long-Term Fairness Inquiries and Pursuits in Machine Learning: A Survey of Notions, Methods, and Challenges
Gohar, Usman, Tang, Zeyu, Wang, Jialu, Zhang, Kun, Spirtes, Peter L., Liu, Yang, Cheng, Lu
–arXiv.org Artificial Intelligence
While dynamic influential roles in high-stake domains traditionally steered fairness aligns with this concept by considering by human judgments, an extensive body of research has evolving dynamics over time (Li et al. 2023), long-term fairness brought attention to the challenges of bias and discrimination has a much broader scope. This umbrella term has different against marginalized groups (Mehrabi et al. 2021; facets, including sequential fairness (where sequential Cheng, Varshney, and Liu 2021). These issues are pervasive decisions impact fairness) and fairness over multiple time and manifest in different settings, including finance, steps, among others (as depicted in Fig:1). In this work, we legal (e.g., pretrial bail decisions), aviation, and healthcare aim to unify the different strands of literature on long-term practices, among others (Gohar et al. 2024; Barocas, Hardt, fairness under a common framework.
arXiv.org Artificial Intelligence
Jun-10-2024
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