| Discrimination through optimization: How Facebook's Ad delivery can lead to biased outcomes M Ali, P Sapiezynski, M Bogen, A Korolova, A Mislove, A Rieke Proceedings of the ACM on human-computer interaction 3 (CSCW), 1-30, 2019 | 328 | 2019 |
| Help wanted: An examination of hiring algorithms, equity, and bias M Bogen, A Rieke Upturn, 2018 | 165* | 2018 |
| All the ways hiring algorithms can introduce bias M Bogen Harvard Business Review 6, 2019, 2019 | 66 | 2019 |
| Awareness in practice: tensions in access to sensitive attribute data for antidiscrimination M Bogen, A Rieke, S Ahmed Proceedings of the 2020 conference on fairness, accountability, and …, 2020 | 45 | 2020 |
| Fairness on the ground: Applying algorithmic fairness approaches to production systems C Bakalar, R Barreto, S Bergman, M Bogen, B Chern, S Corbett-Davies, ... arXiv preprint arXiv:2103.06172, 2021 | 16 | 2021 |
| Public scrutiny of automated decisions: Early lessons and emerging methods A Rieke, M Bogen, DG Robinson Upturn, 2018 | 15 | 2018 |
| Adaptive Sampling Strategies to Construct Equitable Training Datasets W Cai, R Encarnacion, B Chern, S Corbett-Davies, M Bogen, S Bergman, ... Proceedings of the 2022 ACM Conference on Fairness, Accountability, and …, 2022 | 12 | 2022 |
| Help Wanted—An Exploration of Hiring Algorithms, Equity and Bias. Upturn M Bogen, A Rieke | 5 | 2018 |
| Leveling the platform: real transparency for paid messages on Facebook A Rieke, M Bogen Upturn, 2018 | 3 | 2018 |
| Casual Conversations v2: Designing a large consent-driven dataset to measure algorithmic bias and robustness C Hazirbas, Y Bang, T Yu, P Assar, B Porgali, V Albiero, S Hermanek, ... arXiv preprint arXiv:2211.05809, 2022 | 1 | 2022 |
| Data ethics: Investing wisely in data at scale D Robinson, M Bogen EFC: European Foundation Centre, 2016 | 1 | 2016 |
