Savannah Thais*, Hannah Shumway, and Austin Iglesias Saragih
Edited by Audrey Bertin and Sarah Radway
Review | Aug. 31 2023
- A large portion of academic literature as well as proposed and enacted US policies commonly focus on quantitative notions of fairness and data bias when they assess algorithmic bias. This has limited practical utility.
- Few algorithmic bias-focused policies have actually been enacted in the United States, but those that have center on temporary bans, transparency, and post-hoc bias audits.
- A holistic and collective approach is likely the most promising for meaningfully addressing issues of algorithmic bias.
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