S. L. Blodgett and B. Connor, Racial disparity in natural language processing: A case study of social media african-american english, 2017.

L. E. Celis and N. K. Vishnoi, , 2017.

F. Chierichetti, R. Kumar, S. Lattanzi, and S. Vassilvitskii, Fair learning in markovian environments, 2017.

A. Chouldechova, Fair prediction with disparate impact: A study of bias in recidivism prediction instruments, 2016.

S. Corbett-davies, E. Pierson, A. Feller, S. Goel, and A. Huq, Algorithmic decision making and the cost of fairness, 2017.

M. O. Duff, Optimal Learning Computational Procedures for Bayes-adaptive Markov Decision Processes, 2002.

C. Dwork, M. Hardt, T. Pitassi, O. Reingold, and R. Zemel, Fairness through awareness, Proceedings of the 3rd Innovations in Theoretical Computer Science Conference, pp.214-226, 2012.

M. Hardt, E. Price, and N. Srebro, Equality of opportunity in supervised learning, pp.3315-3323, 2016.

S. Jabbari, M. Joseph, M. Kearns, J. Morgenstern, R. et al., Fair learning in markovian environments, 2016.

M. Joseph, M. Kearns, J. Morgenstern, S. Neel, R. et al., Rawlsian fairness for machine learning, 2016.

N. Kilbertus, M. Rojas-carulla, G. Parascandolo, M. Hardt, D. Janzing et al., Avoiding discrimination through causal reasoning, 2017.

J. Kleinberg, S. Mullainathan, and M. Raghavan, Inherent trade-offs in the fair determination of risk scores, 2016.

J. Larson, S. Mattu, L. Kirchner, and J. Angwin, Propublica COMPAS git-hub repository, 2016.

M. L. Puterman, Markov Decision Processes : Discrete Stochastic Dynamic Programming, 1994.

C. Russell, M. J. Kusner, J. Loftus, and R. Silva, When worlds collide: integrating different counterfactual assumptions in fairness, Advances in Neural Information Processing Systems, pp.6414-6423, 2017.

M. B. Zafar, I. Valera, M. Gomez-rodriguez, and K. P. Gummadi, Fairness beyond disparate treatment, 2017.