What Should We Do when Our Ideas of Fairness Conflict?

Author:

Raghavan Manish1

Affiliation:

1. MIT Sloan School of Management and Department of Electrical Engineering and Computer Science, Cambridge, MA, USA

Abstract

Standards for fair decision making could help us develop algorithms that comport with our consensus views; however, algorithmic fairness has its limits.

Publisher

Association for Computing Machinery (ACM)

Subject

General Computer Science

Reference38 articles.

1. Abebe , R. et al. Roles for computing in social change . In Proceedings of the Conf. on Fairness, Accountability, and Transparency ( 2020 ), 252--260. Abebe, R. et al. Roles for computing in social change. In Proceedings of the Conf. on Fairness, Accountability, and Transparency (2020), 252--260.

2. Angwin , J. and Larson , J . Bias in criminal risk scores is mathematically inevitable, researchers say. Ethics of Data and Analytics , Auerbach Publications ( 2016 ), 265--267. Angwin, J. and Larson, J. Bias in criminal risk scores is mathematically inevitable, researchers say. Ethics of Data and Analytics, Auerbach Publications (2016), 265--267.

3. Angwin , J. , Larson , J. , Mattu , S. and Kirchner , L . Machine bias. Ethics of Data and Analytics , Auerbach Publications ( 2016 ), 254--264. Angwin, J., Larson, J., Mattu, S. and Kirchner, L. Machine bias. Ethics of Data and Analytics, Auerbach Publications (2016), 254--264.

4. Barabas , C. et al. Interventions over predictions: Reframing the ethical debate for actuarial risk assessment . In Proceedings of the Conf. on Fairness, Accountability, and Transparency ( 2018 ), 62--76. Barabas, C. et al. Interventions over predictions: Reframing the ethical debate for actuarial risk assessment. In Proceedings of the Conf. on Fairness, Accountability, and Transparency (2018), 62--76.

5. Barocas S. Hardt M. and Narayanan A. Fairness and Machine Learning (2019); http://www.fairmlbook.org. Barocas S. Hardt M. and Narayanan A. Fairness and Machine Learning (2019); http://www.fairmlbook.org.

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