Fairer machine learning in the real world: Mitigating discrimination without collecting sensitive data
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DOI: 10.31219/osf.io/ustxg
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References listed on IDEAS
- Veale, Michael, 2017. "Logics and practices of transparency and opacity in real-world applications of public sector machine learning," SocArXiv 6cdhe, Center for Open Science.
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Cited by:
- Alexandru Constantin Ciobanu & Gabriela Meè˜Nièšä‚, 2021. "Ai Ethics In Business €“ A Bibliometric Approach," Review of Economic and Business Studies, Alexandru Ioan Cuza University, Faculty of Economics and Business Administration, issue 28, pages 169-202, December.
- Raghu K Para, 2024. "The Role of Explainable AI in Bias Mitigation for Hyper-personalization," Journal of Artificial Intelligence General science (JAIGS) ISSN:3006-4023, Open Knowledge, vol. 6(1), pages 625-635.
- Moritz Zahn & Stefan Feuerriegel & Niklas Kuehl, 2022. "The Cost of Fairness in AI: Evidence from E-Commerce," Business & Information Systems Engineering: The International Journal of WIRTSCHAFTSINFORMATIK, Springer;Gesellschaft für Informatik e.V. (GI), vol. 64(3), pages 335-348, June.
- Veale, Michael & Brass, Irina, 2019. "Administration by Algorithm? Public Management meets Public Sector Machine Learning," SocArXiv mwhnb, Center for Open Science.
- Arifuzzaman (Arif) Sheikh & Steven J. Simske & Edwin K. P. Chong, 2024. "Evaluating Artificial Intelligence Models for Resource Allocation in Circular Economy Digital Marketplace," Sustainability, MDPI, vol. 16(23), pages 1-39, December.
- Veale, Michael & Van Kleek, Max & Binns, Reuben, 2018. "Fairness and Accountability Design Needs for Algorithmic Support in High-Stakes Public Sector Decision-Making," SocArXiv 8kvf4_v1, Center for Open Science.
- Veale, Michael & Van Kleek, Max & Binns, Reuben, 2018. "Fairness and Accountability Design Needs for Algorithmic Support in High-Stakes Public Sector Decision-Making," SocArXiv 8kvf4, Center for Open Science.
- Alina Köchling & Marius Claus Wehner, 2020. "Discriminated by an algorithm: a systematic review of discrimination and fairness by algorithmic decision-making in the context of HR recruitment and HR development," Business Research, Springer;German Academic Association for Business Research, vol. 13(3), pages 795-848, November.
- Matus, Kira & Veale, Michael, 2021. "Certification Systems for Machine Learning: Lessons from Sustainability," SocArXiv pm3wy, Center for Open Science.
- Veale, Michael & Binns, Reuben & Van Kleek, Max, 2018. "Some HCI Priorities for GDPR-Compliant Machine Learning," LawArXiv wm6yk, Center for Open Science.
- Irene Unceta & Jordi Nin & Oriol Pujol, 2020. "Risk mitigation in algorithmic accountability: The role of machine learning copies," PLOS ONE, Public Library of Science, vol. 15(11), pages 1-26, November.
- Veale, Michael & Binns, Reuben & Van Kleek, Max, 2018. "Some HCI Priorities for GDPR-Compliant Machine Learning," LawArchive wm6yk_v1, Center for Open Science.
- Brielle Lillywhite & Gregor Wolbring, 2022. "Emergency and Disaster Management, Preparedness, and Planning (EDMPP) and the ‘Social’: A Scoping Review," Sustainability, MDPI, vol. 14(20), pages 1-50, October.
- Kira J.M. Matus & Michael Veale, 2022. "Certification systems for machine learning: Lessons from sustainability," Regulation & Governance, John Wiley & Sons, vol. 16(1), pages 177-196, January.
- Simerta Gill & Gregor Wolbring, 2022. "Auditing the ‘Social’ Using Conventions, Declarations, and Goal Setting Documents: A Scoping Review," Societies, MDPI, vol. 12(6), pages 1-100, October.
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