Bias Mitigation Post-processing for Individual and Group Fairness

C. K. Chow, “On optimum recognition error and reject tradeoff,” IEEE Trans. Inf. Theory , vol. IT-16, no. 1, pp. 41–46, Jan. 1970

1970

Earlier work this paper cites.

P. Godefroid, N. Klarlund, and K. Sen, “DART: Directed automated random testing,” in Proc. ACM SIGPLAN Conf. Program. Lang. Design Impl. , Chicago, USA, 2005, pp. 213–223

2005

Earlier work this paper cites.

N. Hurley and S. Rickard, “Comparing measures of sparsity,” IEEE Trans. Inf. Theory , vol. 55, no. 10, pp. 4723–4741, Oct. 2009

2009

Earlier work this paper cites.

K. R. Varshney, “A risk bound for ensemble classification with a reject option,” in Proc. IEEE Stat. Signal Process. Workshop , Nice, France, Jun. 2011, pp. 769–772

2011

Earlier work this paper cites.

F. Kamiran, A. Karim, and X. Zhang, “Decision theory for discrimination-aware classification,” in Proc. IEEE Int. Conf. Data Min. , Brussels, Belgium, Dec. 2012, pp. 924–929

2012

Earlier work this paper cites.

C. Dwork, M. Hardt, T. Pitassi, O. Reingold, and R. Zemel, “Fairness through awareness,” in Proc. Innov. Theo. Comp. Sci. Conf. , Cambridge, USA, Jan. 2012, pp. 214–226

2012

Earlier work this paper cites.

R. Zemel, Y. L. Wu, K. Swersky, T. Pitassi, and C. Dwork, “Learning fair representations,” in Proc. Int. Conf. Mach. Learn. , Atlanta, USA, Jun. 2013, pp. 325–333

2013

Earlier work this paper cites.

M. Hardt, E. Price, and N. Srebro, “Equality of opportunity in supervised learning,” in Adv. Neur. Inf. Process. Syst. 29 , Barcelona, Spain, Dec. 2016, pp. 3315–3323

2016

Earlier work this paper cites.

L. R. Varshney and K. R. Varshney, “Decision making with quantized priors leads to discrimination,” Proc. IEEE , vol. 105, no. 2, pp. 241–255, Feb. 2017

2017

Earlier work this paper cites.

K. R. Varshney and H. Alemzadeh, “On the safety of machine learning: Cyber-physical systems, decision sciences, and data products,” Big Data , vol. 5, no. 3, pp. 246–255, Sep. 2017

2017

Cited alongside, same era.

I. Źliobaităź, “Measuring discrimination in algorithmic decision making,” Data Min. Knowl. Disc. , vol. 31, no. 4, pp. 1060–1089, Jul. 2017

2017

Cited alongside, same era.

S. Galhotra, Y. Brun, and A. Meliou, “Fairness testing: Testing software for discrimination,” in Proc. Joint Meeting Found. Softw. Eng. , Paderborn, Germany, Sep. 2017, pp. 498–510

2017

Cited alongside, same era.

B. d’Alessandro, C. O’Neil, and T. LaGatta, “Conscientious classification: A data scientist’s guide to discrimination-aware classification,” Big Data , vol. 5, no. 2, pp. 120–134, Jun. 2017

2017

Cited alongside, same era.

F. P. Calmon, D. Wei, B. Vinzamuri, K. N. Ramamurthy, and K. R. Varshney, “Data pre-processing for discrimination prevention: Information-theoretic optimization and analysis,” IEEE J. Sel. Topics Signal Process. , vol. 12, no. 5, pp. 1106–1119, Oct. 2018

2018

Closest in time.

A. Narayanan, “Translation tutorial: 21 fairness definitions and their politics,” in Proc. Conf. Fairness Accountability Transp. , New York, USA, Feb. 2018

2018

Closest in time.

A. Agarwal, P. Lohia, S. Nagar, K. Dey, and D. Saha, “Automated test generation to detect individual discrimination in AI models,” arXiv:1809.03269, Sep. 2018

Original

2018

Closest in time.

T. Speicher, H. Heidari, N. Grgic-Hlaca, K. P. Gummadi, A. Singla, A. Weller, and M. B. Zafar, “A unified approach to quantifying algorithmic unfairness: Measuring individual & group unfairness via inequality indices,” in Proc. ACM SIGKDD Int. Conf. Knowl. Disc. Data Min. , London, UK, Aug. 2018, pp. 2239–2248

2018