Fair Prediction with Disparate Impact: A Study of Bias in Recidivism Prediction Instruments

Big Data

Volumn 5, Issue 2, 2017, Pages 153-163

  Chouldechova, Alexandra ^a  

^a CARNEGIE MELLON UNIVERSITY   (United States)

Author keywords

bias; disparate impact; fair machine learning; recidivism prediction; risk assessment

Indexed keywords

DECISION MAKING; EMPIRICAL RESEARCH; HUMAN; RISK ASSESSMENT; THEORETICAL MODEL;

DECISION MAKING; EMPIRICAL RESEARCH; HUMANS; MODELS, THEORETICAL; RISK ASSESSMENT;

EID: 85021102916     PISSN: 21676461     EISSN: 2167647X     Source Type: Journal    
DOI: 10.1089/big.2016.0047     Document Type: Article

References (34)

1. Model penal code: Sentencing. American Law Institute. 2016. Available at: https://www. ali. org/projects/show/sentencing (last accessed September 14, 2016).
2. Blomberg T, Bales W, Mann K, et al. Validation of the compas risk assessment classification instrument. 2010. Available at: http:// criminology. fsu. edu/wp-content/uploads/Validation-of-the-COMPASRisk-Assessment-Classification-Instrument. pdf (last accessed September 14, 2016).
3. Casselman B, Barry-Jester AM, Goldstein D. Should prison sentences be based on crimes that haven't been committed yet Available at: https:// fivethirtyeight. com/features/prison-reform-risk-assessment (last accessed September 14, 2016).
4. Angwin J, Larson J, Mattu S, Kirchner L. 2016. Machine bias: There's software used across the country to predict future criminals and it's biased against blacks. Available online at www. propublica. org/article/ machine-bias-risk-assessments-in-criminal-sentencing (last accessed February 6, 2017).
5. Northpointe. Compas risk & need assessment system: Selected questions posed by inquiring agencies. Available at: http://www. northpointeinc . com/files/technical-documents/Selected-Compas-Questions-Posed- by-Inquiring-Agencies. pdf (last accessed September 14, 2016).
6. Flores AW, Bechtel K, Lowenkamp CT. False positives, false negatives, and false analyses: A rejoinder to ''machine bias: There's software used across the country to predict future criminals. And it's biased against blacks. 2016. Available at: http://www. crj. org/page/-/publications/ rejoinder7. 11. pdf
7. Dieterich W, Mendoza C, Brennan T. Compas risk scales: Demonstrating accuracy equity and predictive parity. 2016. Available at: http://go . volarisgroup. com/rs/430-MBX-989/images/ProPublica-Commentary- Final-070616. pdf (last accessed February 6, 2017).
8. Angwin J, Larson J, Mattu S, Kirchner L. 2016. How we analyzed the compas recidivism algorithm. Available online at www. propublica. org/ article/how-we-analyzed-the-compas-recidivism-algorithm (last accessed February 6, 2017).
9. Administrative Office of the United States Courts. An overview of the federal post conviction risk assessment, September 2011.
10. Singh JP. Predictive validity performance indicators in violence risk assessment: A methodological primer. Behav Sci Law. 2013;310:0 8-22.
11. Skeem JL, Lowenkamp CT. Risk, race, & recidivism: Predictive bias and disparate impact. Criminology. 2016;54:680-712.
12. Skeem JL, Monahan J, Lowenkamp CT. Gender, risk assessment, and sanctioning: The cost of treating women like men. Law Hum Behav. 2016;40:580-593.
13. Hardt M, Price E, Srebro N, et al. Equality of opportunity in supervised learning. In: Advances in Neural Information Processing Systems. NIPS, 2016, pp. 3315-3323.
14. Zliobaite I. On the relation between accuracy and fairness in binary classification. arXiv preprint arXiv:1505. 05723, 2015.
15. Dwork C, Hardt M, Pitassi T, et al. Fairness through awareness. In: Proceedings of the 3rd Innovations in Theoretical Computer Science Conference. ACM, 2012, pp. 214-226.
16. Calders T, Verwer S. Three naive bayes approaches for discrimination-free classification. Data Mining Knowl Discov. 2010;210:277-292.
17. Fish B, Kun J, Lelkes Á D. A confidence-based approach for balancing fairness and accuracy. In: Proceedings of the 2016 SIAM International Conference on Data Mining. SIAM, 2016, pp. 144-152.
18. Romei A, Ruggieri S. A multidisciplinary survey on discrimination analysis. T Knowl Eng Rev. 2014;290:582-638.
19. Barocas S, Selbst AD. Big data's disparate impact. Cal Law Rev. 2016;104:671.
20. Hannah-Moffat K. Actuarial sentencing: An unsettled proposition. Justice Quart. 2013;300:270-296.
21. Skeem J. Risk technology in sentencing: Testing the promises and perils (commentary on hannah-moffat, 2011). Justice Quart. 2013;300:297-303.
22. Monahan J, Skeem JL. Risk assessment in criminal sentencing. Annu Rev Clin Psychol. 2016;12:489-513.
23. Kleinberg J, Mullainathan S, Raghavan M. Inherent trade-offs in the fair determination of risk scores. arXiv preprint arXiv:1609. 05807, 2016.
24. Corbett-Davies S, Pierson E, Feller A, Goel S. A computer program used for bail and sentencing decisions was labeled biased against blacks. It's actually not that clear. Available at: https://www. washingtonpost. com/ news/monkey-cage/wp/2016/10/17/can-an-Algorithm-be-racist-ouranalysis-is-more-cautious-than-propublicas/utm-term=. b7e706ae3362 (last accessed February 2, 2017).
25. Ritter N. Predicting recidivism risk: New tool in Philadelphia shows great promise. Natl Inst Just J 271, 2013.
26. PCS. Validation of risk scale. Technical report, Pennsylvania Commission on Sentencing, 2013.
27. Cohen J. Statistical power analysis for the behavioral sciences (2nd edition). Mahwah, NJ: Lawrence Erlbaum Associates, 1988.
28. Uniform crime report: Crime in the United States, 2015-offenses cleared. U. S. Department of Justice, 2016.
29. Meehl PE. Clinical versus statistical prediction: A theoretical analysis and a review of the evidence. Minneapolis, MN: University of Minnesota Press, 1954.
30. Dawes RM, Faust D, Meehl PE. Clinical versus actuarial judgment. Science. 1989;243:1668-1674.
31. Grove WM, Zald DH, Lebow BS, et al. Clinical versus mechanical prediction: A meta-analysis. Psychol Assess. 2000;12:19.
32. Kleinberg J, Lakkaraju H, Leskovec J, Ludwig J, Mullainathan S. Human decisions and machine predictions. Technical report, National Bureau of Economic Research. 2017.
33. Anwar S, Fang H. Testing for racial prejudice in the parole board release process: Theory and evidence. Technical report, National Bureau of Economic Research. 2012.
34. Sweeney LT, Haney C. The influence of race on sentencing: A meta-analytic review of experimental studies. Behav Sci Law. 1992;100:179-195.