Abstract
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.”
| Original language | English |
|---|---|
| Pages (from-to) | 87-94 |
| Number of pages | 8 |
| Journal | IEEE Technology and Society Magazine |
| Volume | 42 |
| Issue number | 4 |
| DOIs | |
| Publication status | Published - 19 Jan 2024 |
| Externally published | Yes |
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