Inherent trade-offs in the fair determination of risk scores

Leibniz International Proceedings in Informatics, LIPIcs

Volumn 67, Issue , 2017, Pages

  Kleinberg, Jon ^a   Mullainathan, Sendhil ^b   Raghavan, Manish ^b  

^a Department of Obstetrics Gynecology   (United States)

^b HARVARD UNIVERSITY   (United States)

Author keywords

algorithmic fairness; calibration; risk tools

Indexed keywords

CALIBRATION; COMMERCE;

ALGORITHMIC FAIRNESS; PROBABILISTIC CLASSIFICATION; RISK SCORE; TRADE OFF;

ECONOMIC AND SOCIAL EFFECTS;

EID: 85021028155     PISSN: 18688969     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.4230/LIPIcs.ITCS.2017.43     Document Type: Conference Paper

References (20)

1. Julia Angwin, Jeff Larson, Surya Mattu, and Lauren Kirchner. Machine bias: Theres software used across the country to predict future criminals. And its biased against blacks. ProPublica, May 23, 2016. URL: https://www.propublica.org/article/machine-bias-risk-Assessments-in-criminal-sentencing
2. Solon Barocas and Andrew D Selbst. Big datas disparate impact. California Law Review, 104, 2016
3. Toon Calders and Sicco Verwer. Three naive bayes approaches for discrimination-free classification. Data Mining and Knowledge Discovery, 21(2):277-292, 2010
4. Cynthia S. Crowson, Elizabeth J. Atkinson, and Terry M. Therneau. Assessing calibration of prognostic risk scores. Statistical Methods in Medical Research, 25(4):1692-1706, 2016
5. Amit Datta, Michael Carl Tschantz, and Anupam Datta. Automated experiments on ad privacy settings. Proceedings on Privacy Enhancing Technologies, 2015(1):92-112, 2015. URL: http://www.degruyter.com/view/j/popets.2015.1 issue-1/popets-2015-0007/popets-2015-0007.xml
6. William Dieterich, Christina Mendoza, and Tim Brennan. COMPAS risk scales: Demonstrating accuracy equity and predictive parity. Technical report, Northpointe, July 2016 URL: http://www.northpointeinc.com/northpointe-Analysis
7. Cynthia Dwork, Moritz Hardt, Toniann Pitassi, Omer Reingold, and Richard S. Zemel. Fairness through awareness. In Innovations in Theoretical Computer Science 2012, Cambridge, MA, USA, January 8-10, 2012, pages 214-226, 2012. doi:10.1145/2090236.2090255
8. Michael Feldman, Sorelle A. Friedler, John Moeller, Carlos Scheidegger, and Suresh Venkatasubramanian Certifying and removing disparate impact. In Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 15, pages 259-268, New York, NY, USA, 2015. ACM. doi:10.1145/2783258.2783311
9. Anthony Flores, Christopher Lowenkamp, and Kristin Bechtel. False positives, false negatives, and false analyses: A rejoinder to "machine bias: Theres software used across the country to predict future criminals. and its biased against blacks.". Technical report, Crime & Justice Institute, September 2016. URL: http://www.crj.org/cji/entry/false-positives-false-negatives-And-false-Analyses-A-rejoinder
10. Dean P. Foster and Rakesh V. Vohra. Asymptotic calibration. Biometrika, 85(2):379-390, 1998
11. Howard N. Garb. Race bias, social class bias, and gender bias in clinical judgment. Clinical Psychology: Science and Practice, 4(2):99-120, 1997
12. Abe Gong. Ethics for powerful algorithms (1 of 4). Medium, July 12, 2016. URL: https: //medium.com/@AbeGong/ethics-for-powerful-Algorithms-1-of-3-A060054efd84# .dhsd2ut3i
13. Faisal Kamiran and Toon Calders. Classifying without discriminating. 2009 2nd International Conference on Computer, Control and Communication, IC4 2009, 2009. doi: 10.1109/IC4.2009.4909197
14. Toshihiro Kamishima, Shotaro Akaho, and Jun Sakuma. Fairness-Aware learning through regularization approach. Proceedings-IEEE International Conference on Data Mining, ICDM, pages 643-650, 2011. doi:10.1109/ICDMW.2011.83
15. Jeff Larson, Surya Mattu, Lauren Kirchner, and Julia Angwin. How we analyzed the COMPAS recidivism algorithm. ProPublica, May 23, 2016. URL: https://www.propublica org/article/how-we-Analyzed-The-compas-recidivism-Algorithm
16. Executive Office of the President. Big data: A report on algorithmic systems, opportunity, and civil rights. Technical report, The White House, Washington, USA, May 2016
17. Propublica analysis. URL: https://docs.google.com/document/d/1pKtyl8XmJH7Z09lxkb70n6fa2Fiitd7ydbxgCT-wCXs/edit?pref=2&pli=1
18. Latanya Sweeney. Discrimination in online ad delivery. Communications of the ACM, 56(5):44-54, 2013. doi:10.1145/2447976.2447990
19. David R. Williams and Selina A. Mohammed. Discrimination and racial disparities in health: Evidence and needed research. J. Med. Behav., 32(1), 2009
20. Richard S Zemel, Yu Wu, Kevin Swersky, Toniann Pitassi, and Cynthia Dwork. Learning Fair Representations. Proceedings of the 30th International Conference on Machine Learning, 28:325-333, 2013. URL: http://jmlr.org/proceedings/papers/v28/zemel13.html