Fair AI
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DOI: 10.1007/s12599-020-00650-3
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- Wil M. P. Aalst & Martin Bichler & Armin Heinzl, 2017. "Responsible Data Science," Business & Information Systems Engineering: The International Journal of WIRTSCHAFTSINFORMATIK, Springer;Gesellschaft für Informatik e.V. (GI), vol. 59(5), pages 311-313, October.
- Katharina Hamann & Felix Warneken & Julia R. Greenberg & Michael Tomasello, 2011. "Collaboration encourages equal sharing in children but not in chimpanzees," Nature, Nature, vol. 476(7360), pages 328-331, August.
- Mehmet Eren Ahsen & Mehmet Ulvi Saygi Ayvaci & Srinivasan Raghunathan, 2019. "When Algorithmic Predictions Use Human-Generated Data: A Bias-Aware Classification Algorithm for Breast Cancer Diagnosis," Service Science, INFORMS, vol. 30(1), pages 97-116, March.
- repec:nas:journl:v:115:y:2018:p:e3635-e3644 is not listed on IDEAS
- Kraus, Mathias & Feuerriegel, Stefan & Oztekin, Asil, 2020. "Deep learning in business analytics and operations research: Models, applications and managerial implications," European Journal of Operational Research, Elsevier, vol. 281(3), pages 628-641.
- James Zou & Londa Schiebinger, 2018. "AI can be sexist and racist — it’s time to make it fair," Nature, Nature, vol. 559(7714), pages 324-326, July.
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