This repository aims to provide recent advance in auditing machine learning in terms of fairness, privacy, robustness, and so on.
If you are also intereste in this field, welcome to contact and discusss！
[1]. Fairness Audit of Machine Learning Models with Confidential Computing.[paper] [code]
Saerom Park, Seongmin Kim, Yeon-sup Lim. Sungshin Women’s University. WWW, 2022.
[2]. Challenges in Translating Research to Practice for Evaluating Fairness and Bias in Recommendation Systems. [paper] [code]
Lex Beattie, Dan Taber, Henriette Cramer. Spotify. RecSys, 2022.
[3]. Active Fairness Auditing. [paper] [code]
Tom Yan, Chicheng Zhang. Carnegie Mellon University/University of Arizona. ICML, 2022.
[1]. Aequitas: A Bias and Fairness Audit Toolkit.[paper] [code]
Pedro Saleiro, Benedict Kuester, Loren Hinkson, Jesse London, Abby Stevens, Ari Anisfeld, Kit T. Rodolfa, Rayid Ghani. University of Chicago. Arxiv, 2019.