부산대학교 도서관

Algorithmic Decision-Making Systems (ADMS) 1 fairness issues have been well highlighted over the past decade [1], including some facial recognition systems struggling to identify people of color [2]. In 2021, Uber drivers filed a claim with the U.K. ’s employment tribunal for unfair dismissal resulting from automated facial recognition technology by Microsoft [3]. Bias mitigation methods have been developed to reduce discrimination from ADMS. These typically operationalize fairness notions as fairness metrics to minimize discrimination [4]. We refer to ADMS to which bias mitigation methods have been applied as “mitigated ADMS” or, in the singular, a “mitigated system.”