Participation and Division of Labor in User-Driven Algorithm Audits: How Do Everyday Users Work together to Surface Algorithmic Harms?

Author:

Li Rena1ORCID,Kingsley Sara1ORCID,Fan Chelsea1ORCID,Sinha Proteeti2ORCID,Wai Nora1ORCID,Lee Jaimie1ORCID,Shen Hong3ORCID,Eslami Motahhare3ORCID,Hong Jason3ORCID

Affiliation:

1. Carnegie Mellon University, United States

2. School of Computer Science, Carnegie Mellon University, United States

3. Human-Computer Interaction Institute, Carnegie Mellon University, United States

Funder

National Science Foundation

Amazon Research Award

Cisco Research Grant

Publisher

ACM

Reference78 articles.

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2. 2020. Twitter investigates racial bias in image previews. BBC News (Sep 2020 ). https://www.bbc.com/news/technology-54234822 2020. Twitter investigates racial bias in image previews. BBC News (Sep 2020). https://www.bbc.com/news/technology-54234822

3. Parag Agrawal and Dantley Davis . 2020. Transparency around image cropping and changes to come. Twitter ( 2020 ). https://blog.twitter.com/en_us/topics/product/2020/transparency-image-cropping.html Parag Agrawal and Dantley Davis. 2020. Transparency around image cropping and changes to come. Twitter (2020). https://blog.twitter.com/en_us/topics/product/2020/transparency-image-cropping.html

4. Mike Ananny and Kate Crawford . 2018. Seeing without knowing: Limitations of the transparency ideal and its application to algorithmic accountability. new media & society 20, 3 ( 2018 ), 973–989. Mike Ananny and Kate Crawford. 2018. Seeing without knowing: Limitations of the transparency ideal and its application to algorithmic accountability. new media & society 20, 3 (2018), 973–989.

5. “It’s Complicated”: Negotiating Accessibility and (Mis)Representation in Image Descriptions of Race, Gender, and Disability

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3. Bound by the Bounty: Collaboratively Shaping Evaluation Processes for Queer AI Harms;Proceedings of the 2023 AAAI/ACM Conference on AI, Ethics, and Society;2023-08-08

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