Supporting User Engagement in Testing, Auditing, and Contesting AI
Author:
Affiliation:
1. Carnegie Mellon University, United States
2. Stanford University, United States
3. University of Pennsylvania, United States
Funder
NSF (National Science Foundation)
Publisher
ACM
Link
https://dl.acm.org/doi/pdf/10.1145/3584931.3611279
Reference37 articles.
1. Open AI. 2022. ChatGPT Feedback Contest: Official Rules. https://cdn.openai.com/chatgpt/ChatGPT_Feedback_Contest_Rules.pdf Open AI. 2022. ChatGPT Feedback Contest: Official Rules. https://cdn.openai.com/chatgpt/ChatGPT_Feedback_Contest_Rules.pdf
2. Contestable Camera Cars: A Speculative Design Exploration of Public AI That Is Open and Responsive to Dispute
3. Beat the Machine
4. Joy Buolamwini and Timnit Gebru . 2018 . Gender shades: Intersectional accuracy disparities in commercial gender classification . In Conference on fairness, accountability and transparency. PMLR, 77–91 . Joy Buolamwini and Timnit Gebru. 2018. Gender shades: Intersectional accuracy disparities in commercial gender classification. In Conference on fairness, accountability and transparency. PMLR, 77–91.
5. Discovering and Validating AI Errors With Crowdsourced Failure Reports
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