Xiang, Alice

Explainable machine learning in deployment

Bhatt, U., Xiang, A., Sharma, S., Weller, A., Taly, A., Jia, Y., Ghosh, J., Puri, R., Moura, J. M. F., & Eckersley, P. (2020) · Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency

Explainable Artificial Intelligence (XAI)Ethics and Social Impacts of AI

Cited by 633

Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models

Srivastava, A., Rastogi, A., Rao, A., Shoeb, A. A. M., Abid, A., Fisch, A., Brown, A. R., Santoro, A., Gupta, A., Garriga-Alonso, A., et al. (2022) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 569

Uncertainty as a Form of Transparency: Measuring, Communicating, and Using Uncertainty

Bhatt, U., Antorán, J., Zhang, Y., Liao, Q. V., Sattigeri, P., Fogliato, R., Melançon, G., Krishnan, R., Stanley, J., Tickoo, O., et al. (2021) · Proceedings of the 2021 AAAI/ACM Conference on AI, Ethics, and Society

Explainable Artificial Intelligence (XAI)Ethics and Social Impacts of AI

Cited by 300

What We Can't Measure, We Can't Understand: Challenges to Demographic Data Procurement in the Pursuit of Fairness

Andrus, M., Spitzer, E., Brown, J., & Xiang, A. (2021) · Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency

Ethics and Social Impacts of AIExplainable Artificial Intelligence (XAI)

Cited by 111

Machine Learning Explainability for External Stakeholders

Bhatt, U., Andrus, M., Weller, A., & Xiang, A. (2020) · arXiv

Explainable Artificial Intelligence (XAI)Ethics and Social Impacts of AI

Cited by 41

On the Legal Compatibility of Fairness Definitions

Xiang, A., & Raji, I. D. (2019) · arXiv

Ethics and Social Impacts of AIArtificial Intelligence in Law

Cited by 34