| Explainable machine learning in deployment U Bhatt, A Xiang, S Sharma, A Weller, A Taly, Y Jia, J Ghosh, R Puri, ... Proceedings of the 2020 Conference on Fairness, Accountability, and …, 2020 | 64 | 2020 |
| On the Legal Compatibility of Fairness Definitions A Xiang, ID Raji arXiv preprint arXiv:1912.00761, 2019 | 9 | 2019 |
| Assessing the potential impact of a nationwide class-based affirmative action system A Xiang, DB Rubin Statistical Science, 297-327, 2015 | 9 | 2015 |
| Unlocking the potential of art investment vehicles A Xiang Yale LJ 127, 1698, 2017 | 7 | 2017 |
| Machine Learning Explainability for External Stakeholders U Bhatt, MK Andrus, A Weller, A Xiang arXiv preprint arXiv:2007.05408, 2020 | 5 | 2020 |
| Reconciling Legal and Technical Approaches to Algorithmic Bias A Xiang Tennessee Law Review 88 (3), 2021, 2020 | 2 | 2020 |
| Uncertainty as a Form of Transparency: Measuring, Communicating, and Using Uncertainty U Bhatt, Y Zhang, J Antorán, QV Liao, P Sattigeri, R Fogliato, ... arXiv preprint arXiv:2011.07586, 2020 | 1 | 2020 |
| " What We Can't Measure, We Can't Understand": Challenges to Demographic Data Procurement in the Pursuit of Fairness MK Andrus, E Spitzer, J Brown, A Xiang arXiv preprint arXiv:2011.02282, 2020 | 1 | 2020 |
| Affirmative Algorithms: The Legal Grounds for Fairness as Awareness DE Ho, A Xiang arXiv preprint arXiv:2012.14285, 2020 | | 2020 |
