. Ai-now-institute, Litigating algorithms: Challenging government use of algorithmic decision systems, 2018.

M. Alexander, The New Jim Crow: Mass Incarceration in the Age of Colorblindness, 2012.

D. A. Andrews and J. Bonta, Level of Service Inventory -Revised. MultiHealth Systems Toronto, 2000.

D. A. Andrews, J. Bonta, and J. S. Wormith, The recent past and near future of risk and/or need assessment, Crime & Delinquency, vol.52, issue.1, pp.7-27, 2006.

J. Angwin, J. Larson, S. Mattu, and L. Kirchner, Machine Bias: There's software used across the country to predict future criminals. And it's biased against blacks, 2016.

D. Arnold, W. Dobbie, and C. S. Yang, Racial bias in bail decisions, The Quarterly Journal of Economics, vol.133, pp.1885-1932, 2018.

R. Berk, H. Heidari, S. Jabbari, M. Kearns, and A. Roth, Fairness in criminal justice risk assessments: The state of the art, Sociological Methods & Research, 2018.

M. Bogen and A. Rieke, Help wanted: An examination of hiring algorithms, equity, and bias, 2018.

T. Brennan, W. Dieterich, and B. Ehret, Evaluating the predictive validity of the COMPAS risk and needs assessment system, Criminal Justice and Behavior, vol.36, issue.1, pp.21-40, 2009.

A. Byanjankar, M. Heikkilä, and J. Mezei, Predicting credit risk in peer-topeer lending: A neural network approach, IEEE Symposium Series on Computational Intelligence, pp.719-725, 2015.

S. Chiappa, Path-specific counterfactual fairness, Thirty-Third AAAI Conference on Artificial Intelligence, 2019.

A. Chouldechova, Fair prediction with disparate impact: A study of bias in recidivism prediction instruments, vol.5, pp.153-163, 2017.

A. Chouldechova, E. Putnam-hornstein, D. Benavides-prado, O. Fialko, and R. Vaithianathan, A case study of algorithm-assisted decision making in child maltreatment hotline screening decisions, Proceedings of Machine Learning Research, vol.81, pp.134-148, 2018.

S. Corbett-davies, E. Pierson, A. Feller, and S. Goel, Algorithmic decision making and the cost of fairness, Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp.797-806, 2017.

S. Corbett-davies, E. Pierson, A. Feller, S. Goel, and A. Huq, A computer program used for bail and sentencing decisions was labeled biased against blacks. It's actually not that clear, 2016.

S. Corbett-davies and S. Goel, The measure and mismeasure of fairness: A critical review of fair machine learning, 2018.

P. Dawid, Fundamentals of statistical causality, 2007.

J. De-fauw, J. R. Ledsam, B. Romera-paredes, S. Nikolov, N. Tomasev et al., Clinically applicable deep learning for diagnosis and referral in retinal disease, Nature Medicine, vol.24, issue.9, pp.1342-1350, 2018.

W. Dieterich, C. Mendoza, and T. Brennan, COMPAS risk scales: Demonstrating accuracy equity and predictive parity, 2016.

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.

L. Eckhouse, K. Lum, C. Conti-cook, and J. Ciccolini, Layers of bias: A unified approach for understanding problems with risk assessment, Criminal Justice and Behavior, 2018.

V. Eubanks, Automating Inequality: How High-Tech Tools Profile, Police, and Punish the Poor, 2018.

M. Feldman, S. A. Friedler, J. Moeller, C. Scheidegger, and S. Venkatasubramanian, Certifying and removing disparate impact, Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp.259-268, 2015.

A. W. Flores, K. Bechtel, and C. T. Lowenkamp, Machine Bias: There's software used across the country to predict future criminals. And it's biased against blacks, Federal Probation, vol.80, issue.2, pp.38-46, 2016.

H. School, Note: Bail reform and risk assessment: The cautionary tale of federal sentencing, Harvard Law Review, vol.131, issue.4, pp.1125-1146, 2018.

X. He, J. Pan, O. Jin, T. Xu, B. Liu et al., Practical lessons from predicting clicks on ads at facebook, Proceedings of the Eighth International Workshop on Data Mining for Online Advertising, pp.1-9, 2014.

M. Hoffman, L. B. Kahn, and D. Li, Discretion in hiring, The Quarterly Journal of Economics, vol.133, issue.2, pp.765-800, 2018.

W. S. Isaac, Hope, hype, and fear: The promise and potential pitfalls of artificial intelligence in criminal justice, Ohio State Journal of Criminal Law, vol.15, issue.2, pp.543-558, 2017.

J. Kleinberg, S. Mullainathan, and M. Raghavan, Inherent trade-offs in the fair determination of risk scores, 8th Innovations in Theoretical Computer Science Conference, vol.43, pp.1-43, 2016.

J. L. Koepke and D. G. Robinson, Danger ahead: Risk assessment and the future of bail reform, Washington Law Review, p.93, 2017.

K. Kourou, T. P. Exarchos, K. P. Exarchos, M. V. Karamouzis, and D. I. Fotiadis, Machine learning applications in cancer prognosis and prediction, Computational and Structural Biotechnology Journal, vol.13, pp.8-17, 2015.

M. J. Kusner, J. R. Loftus, C. Russell, and R. Silva, Counterfactual fairness, Advances in Neural Information Processing Systems, vol.30, pp.4069-4079, 2017.

K. Lum, Limitations of mitigating judicial bias with machine learning, Nature Human Behaviour, vol.1, issue.7, 2017.

K. Lum and W. Isaac, To predict and serve? Significance, vol.13, pp.14-19, 2016.

S. G. Mayson, Bias in, bias out, Yale Law Journal, 2019.

J. Pearl, Causality: Models, Reasoning, and Inference, 2000.

J. Pearl, M. Glymour, and N. P. Jewell, Causal Inference in Statistics: A Primer, 2016.

C. Perlich, B. Dalessandro, T. Raeder, O. Stitelman, and F. Provost, Machine learning for targeted display advertising: Transfer learning in action, Machine learning, vol.95, issue.1, pp.103-127, 2014.

J. Peters, D. Janzing, and B. Schölkopf, Elements of Causal Inference: Foundations and Learning Algorithms, 2017.

M. Rosenberg and R. Levinson, Trump's catch-and-detain policy snares many who call the, 2018.

A. D. Selbst, Disparate impact in big data policing, Georgia Law Review, vol.52, pp.109-195, 2017.

P. Spirtes, C. N. Glymour, R. Scheines, D. Heckerman, C. Meek et al., Causation, Prediction, and Search, 2000.

M. T. Stevenson, Assessing risk assessment in action, Minnesota Law Review, p.103, 2017.

M. Malekipirbazari and V. Aksakalli, Risk assessment in social lending via random forests, Expert Systems with Applications, vol.42, issue.10, pp.4621-4631, 2015.

R. Vaithianathan, T. Maloney, E. Putnam-hornstein, and N. Jiang, Children in the public benefit system at risk of maltreatment: Identification via predictive modeling, American Journal of Preventive Medicine, vol.45, issue.3, pp.354-359, 2013.

J. Zhang and E. Bareinboim, Fairness in decision-making -the causal explanation formula, Proceedings of the 32nd AAAI Conference on Artificial Intelligence, 2018.