Achieving Universal Fairness in Machine Learning: A Multi-objective Optimization Perspective

Author:

Hu Zirui,Zhang Zheng,Feng Wenjun,Liu Qi

Publisher

Springer Nature Singapore

Reference42 articles.

1. Berk, R.A., Heidari, H., Jabbari, S., Kearns, M., Roth, A.: Fairness in criminal justice risk assessments: the state of the art. Sociol. Methods Res. 50, 3–44 (2017)

2. Bose, A., Hamilton, W.: Compositional fairness constraints for graph embeddings. In: International Conference on Machine Learning, pp. 715–724. PMLR (2019)

3. Calmon, F., Wei, D., Vinzamuri, B., Natesan Ramamurthy, K., Varshney, K.R.: Optimized pre-processing for discrimination prevention. Adv. Neural Inf. Process. Syst. 30 (2017)

4. Chouldechova, A.: Fair prediction with disparate impact: a study of bias in recidivism prediction instruments. Big Data 5(2), 153–163 (2016)

5. Chouldechova, A.: Fair prediction with disparate impact: a study of bias in recidivism prediction instruments. Big Data 5(2), 153–163 (2017)

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