Empirical observation of negligible fairness-precision tradeoffs in machine learning for public policy
Chouldechova, A. Fair prediction with disparate impact: a study of bias in instruments for predicting recidivism. Big data 5, 153-163 (2017).
Skeem, JL & Lowenkamp, CT Risk, race, and recidivism: predictive bias and disparate impact. Criminology 54, 680-712 (2016).
Angwin, J., Larson, J., Mattu, S. & Kirchner, L. Machine bias. ProPublica (May 23, 2016); www.propublica.org/article/machine-bias-risk-assessments-in-criminal-sentencing
Raghavan, M., Barocas, S., Kleinberg, J. & Levy, K. Mitigating Bias in Algorithmic Recruitment: Evaluating Claims and Practices. In Proc. 2020 Conference on Fairness, Accountability and Transparency (eds Hildebrandt, M. & Castillo, C.) 469-481 (ACM, 2020).
Obermeyer, Z., Powers, B., Vogeli, C. & Mullainathan, S. Dissection of racial bias in an algorithm used to manage the health of populations. Science 336, 447-453 (2019).
Ramachandran, A. et al. Predictive analysis for retention in care in an urban HIV clinic. Sci. representing https://doi.org/10.1038/s41598-020-62729-x (2020).
Bauman, MJ et al. Reduce incarceration through priority interventions. In Proc. 1st conference on IT and sustainable societies (COMPASS) (ed. Zegura, E.) 1–8 (ACM, 2018).
Chouldechova, A. et al. A case study of algorithm-assisted decision making in child abuse hotline screening decisions. Proc. Mach. To learn. Res. 81, 134-148 (2018).
Google Scholar
Potash, E. et al. Predictive modeling for public health: preventing childhood lead poisoning. In Proc. 21st ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (eds Cao, L. & Zhang, C.) 2039-2047 (ACM, 2015).
Chen, IY, Johansson, FD & Sontag, D. Why is my classifier discriminatory? In Proc. 32nd International Conference on Neural Information Processing Systems (eds Bengio, S., Wallach, HM, Larochelle, H., Grauman, K. & Cesa-Bianchi, N.) 3539-3550 (NIPS, 2018).
Celis, LE, Huang, L., Keswani, V. & Vishnoi, NK Classification with equity constraints: a meta-algorithm with provable guarantees. In Proc. 2019 Conference on Fairness, Accountability and Transparency (eds Boyd, D. & Morgenstern, J.) 319-328 (ACM, 2019).
Zafar, MB, Valera, I., Rodriguez, MG & Gummadi, KP Equity beyond disparate treatment and disparate impact: Classification learning without disparate abuse. In 26th International World Wide Web Conference (eds Barrett, R. & Cummings, R.) 1171-1180 (WWW, 2017).
Dwork, C., Immorlica, N., Kalai, AT & Leiserson, M. Decoupled classifiers for efficient and fair machine learning. Proc. Mach. To learn. Res. 81, 119-133 (2018).
Google Scholar
Hardt, M., Price, E. & Srebro, N. Equal Opportunity in Supervised Learning. In Proc. 30th International Conference on Neural Information Processing Systems (eds Lee, DD, von Luxburg, U., Garnett, R., Sugiyama, M. & Guyon, I.) 3315–3323 (NIPS, 2016).
Rodolfa, KT et al. Case study: predictive equity to reduce crime recidivism through social service interventions. In Proc. 2020 Conference on Fairness, Accountability and Transparency (eds Hildebrandt, M. & Castillo, C.) 142-153 (ACM, 2020).
Heidari, H., Gummadi, KP, Ferrari, C. & Krause, A. Fairness Behind a Veil of Ignorance: A Well-Being Analysis for Automated Decision Making. In Proc. 32nd International Conference on Neural Information Processing Systems (eds Bengio, S., Wallach, HM, Larochelle, H., Grauman, K. & Cesa-Bianchi, N.) 1265-1276 (NIPS, 2018).
Friedler, SA et al. A Comparative Study of Equity Improvement Interventions in Machine Learning. In Proc. 2019 Conference on Fairness, Accountability and Transparency (eds Boyd, D. & Morgenstern, J.) 329-338 (ACM, 2019).
Kearns, M., Roth, A., Neel, S. & Wu, ZS An empirical study of the equity of rich subgroups for machine learning. In Proc. 2019 Conference on Fairness, Accountability and Transparency (eds Boyd, D. & Morgenstern, J.) 100-109 (ACM, 2019).
Zafar, MB, Valera, I., Rogriguez, MG & Gummadi, KP Equity constraints: mechanisms for fair classification. Proc. 20th International Conference on Artificial Intelligence and Statistics (eds Singh, A. & Zhu, J.) 962-970 (PMLR, 2017).
Ghani, R., Walsh, J. & Wang, J. Top 10 Ways Your Machine Learning Models Can Leak (Data Science for Social Good Blog, 2020); http://www.rayidghani.com/2020/01/24/top-10-ways-your-machine-learning-models-may-have-leakage
Verma, S. & Rubin, J. Definitions of equity explained. In Proc. 2018 International Workshop on Software Fairness (eds Brun, Y., Johnson, B. & Meliou, A.) 1–7 (IEEE / ACM, 2018).
Gajane, P. & Pechenizkiy, M. On formalizing equity in prediction with machine learning. Preprint at https://arxiv.org/abs/1710.03184 (2018).
Kleinberg, JM, Mullainathan, S. & Raghavan, M. Tradeoffs inherent in equitable determination of risk scores. In Proc. 8th Conference on Innovations in Theoretical Computing (ed. Psounis, K.) 1-43 (ITCS, 2017).
Krishna Menon, A. & Williamson, RC The cost of equity in binary classification. In Proc. 1st conference on equity, accountability and transparency (eds Friedler, S. & Wilson, C.) 107–118 (PMLR, 2018).
Huq, A. Racial equity in algorithmic criminal justice. Duke Law J. 68, 1043-1134 (2019).
Google Scholar
Hamilton, M. Persons with Complex Needs and the Criminal Justice System. Court. Iss. Criminal. Justice 22, 307-324 (2010).
James, DJ & Glaze, LE Mental health problems of inmates and inmates (Department of Justice, Bureau of Justice Statistics, 2006); https://www.bjs.gov/content/pub/pdf/mhppji.pdf
Fuller Torrey, E., Kennard, AD, Eslinger, D., Lamb, R. & Pavle, J. There are more mentally ill people in prisons and prisons than in hospitals: a survey of states (Treatment Advocacy Center and National Sheriff’s Association, 2010); http://tulare.networkofcare.org/library/final_jails_v_hospitals_study1.pdf
Holtzen, H., Klein, EG, Keller, B. & Hood, N. Perceptions of physical inspections as a tool to protect housing quality and promote health equity. J. Poor, underserved health care. 27, 549-559 (2016).
Klein, E., Keller, B., Hood, N. & Holtzen, H. Affordable housing and health: an assessment of the health impact of frequency of physical inspections. J. Public health management. Practice 21, 368-374 (2015).
Athey, S. Beyond Prediction: Using Big Data to Solve Policy Problems. Science 355, 483-485 (2017).
Glaeser, EL, Hillis, A., Kominers, SD & Luca, M. Crowdsourcing city government: using tournaments to improve inspection accuracy. A m. Econ. Tower. 106, 114-118 (2016).
Levin, HM & Belfield, C. The Price We Pay: The Economic and Social Consequences of Inadequate Education (Brookings Institution, 2007).
Atwell, MN, Balfanz, R., Bridgeland, J. & Ingram, E. Building a nation of graduates (America’s Promise Alliance, 2019); https://www.americaspromise.org/2019-building-grad-nation-report
Lakkaraju, H. et al. A machine learning framework to identify students at risk for negative academic results. In Proc. 21st ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (eds Cao, L. & Zhang, C.) 1909-1918 (ACM, 2015).
Aguiar, E. et al. Who, when and why: A machine learning approach to prioritize students at risk of not graduating from high school on time. In Proc. Fifth International Conference on Learning Analysis and Knowledge (eds Baron, J., Lynch, G. & Maziarz, N.) 93-102 (ACM, 2015).
Bowers, AJ, Sprott, R. & Taff, SA Do we know who will give up? A review of predictors of early school leaving: precision, sensitivity and specificity. J. 96, 77-100 (2012).
Morgan, I. & Amerikaner, A. 2018 funding gaps (The Education Trust, 2018); https://edtrust.org/wp-content/uploads/2014/09/FundingGapReport_2018_FINAL.pdf
Hurza, M. What do teachers spend on supplies (Adopt a classroom, 2015); https://www.adoptaclassroom.org/2015/09/15/infographic-recent-aac-survey-results-on-teacher-spending/
Ghani, R. Sorting (Center for Data Science and Public Policy, 2016); http://www.datasciencepublicpolicy.org/projects/triage/
Pedregosa, F. et al. Scikit-learn: machine learning in Python. J. Mach. To learn. Res. 12, 2825-2830 (2011).
Roberts, DR et al. Cross-validation strategies for data with a temporal, spatial, hierarchical or phylogenetic structure. Ultrasound 40, 913-929 (2017).
Ye, T. et al. Using machine learning to help vulnerable tenants in New York City. In Proc. 2nd Conference on Computing and Sustainable Societies (COMPASS) (eds Chen, J., Mankoff, J. & Gomes C.) 248-258 (ACM, 2019).
Rodolfa, KT & Lamba, H. dssg / peeps-chili: press release for compromise submission. Zenodo https://doi.org/10.5281/zenodo.5173254 (2021).