| Autor: |
Jorgensen, Mackenzie, Waller, Madeleine, Cocarascu, Oana, Criado, Natalia, Rodrigues, Odinaldo, Such, Jose, Black, Elizabeth |
| Předmět: |
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| Zdroj: |
IEEE Technology & Society Magazine; Dec2023, Vol. 42 Issue 4, p87-94, 8p |
| Abstrakt: |
Algorithmic Decision-Making Systems (ADMS) fairness issues have been well highlighted over the past decade , including some facial recognition systems struggling to identify people of color. 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. Bias mitigation methods have been developed to reduce discrimination from ADMS. These typically operationalize fairness notions as fairness metrics to minimize discrimination. We refer to ADMS to which bias mitigation methods have been applied as “mitigated ADMS” or, in the singular, a “mitigated system.” [ABSTRACT FROM AUTHOR] |
| Databáze: |
Complementary Index |
| Externí odkaz: |
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