Morgenstern, Jamie

Datasheets for Datasets

Gebru, T., Morgenstern, J., Vecchione, B., Vaughan, J. W., Wallach, H., Daumé, H., & Crawford, K. (2018) · arXiv

Research Data Management PracticesScientific Computing and Data Management

Cited by 2,474

Datasheets for datasets

Gebru, T., Morgenstern, J., Vecchione, B., Vaughan, J. W., Wallach, H., III, H. D., & Crawford, K. (2021) · Communications of the ACM

Research Data Management PracticesScientific Computing and Data Management

Cited by 2,469

Fairness in Learning: Classic and Contextual Bandits

Joseph, M., Kearns, M., Morgenstern, J., & Roth, A. (2016) · arXiv

Advanced Bandit Algorithms ResearchMachine Learning and Algorithms

Cited by 199

A Convex Framework for Fair Regression

Berk, R., Heidari, H., Jabbari, S., Joseph, M., Kearns, M., Morgenstern, J., Neel, S., & Roth, A. (2017) · arXiv

Ethics and Social Impacts of AIExplainable Artificial Intelligence (XAI)

Cited by 198

On the Compatibility of Privacy and Fairness

Cummings, R., Gupta, V., Kimpara, D., & Morgenstern, J. (2019) · Adjunct Publication of the 27th Conference on User Modeling, Adaptation and Personalization

Privacy-Preserving Technologies in DataEthics and Social Impacts of AI

Cited by 133

Meritocratic Fairness for Infinite and Contextual Bandits

Joseph, M., Kearns, M., Morgenstern, J., Neel, S., & Roth, A. (2018) · Proceedings of the 2018 AAAI/ACM Conference on AI, Ethics, and Society

Advanced Bandit Algorithms ResearchOptimization and Search Problems

Cited by 24

Preference Dynamics Under Personalized Recommendations

Dean, S., & Morgenstern, J. (2022) · Proceedings of the 23rd ACM Conference on Economics and Computation

Recommender Systems and TechniquesGame Theory and Voting Systems

Cited by 21

FAIRVIS: Visual Analytics for Discovering Intersectional Bias in Machine Learning

Cabrera, Á. A., Epperson, W., Hohman, F., Kahng, M., Morgenstern, J., & Chau, D. H. (2019) · 2019 IEEE Conference on Visual Analytics Science and Technology (VAST)

Ethics and Social Impacts of AIData Visualization and Analytics

Cited by 14

Who's in and who's out? A case study of multimodal CLIP-filtering in DataComp

Hong, R., Agnew, W., Kohno, T., & Morgenstern, J. (2024) · Proceedings of the 4th ACM Conference on Equity and Access in Algorithms, Mechanisms, and Optimization

Ethics and Social Impacts of AIHate Speech and Cyberbullying Detection

Cited by 11