Auditing the COMPAS Recidivism Risk Assessment Tool: Predictive Modelling and Algorithmic Fairness in CS1
Pages 535 - 536
Abstract
We present an assignment in which students apply predictive modelling to build a model that predicts re-arrest of criminal defendants using real data. Students assess the algorithmic fairness of a real-world criminal risk assessment tool (RAT), and reproduce results from an impactful story in ProPublica and a 2018 Science Advances paper. Students explore different measures of algorithmic fairness, and adjust the model they build to satisfy the false positive parity measure.
Our target audience is students in Introduction to Data Science courses that do not require previous computing experience, as well as students in standard CS1 courses. We advocate for teaching predictive modelling in CS1. To facilitate the teaching of predictive modelling in CS1, we provide tutorials on predictive modelling and algorithmic fairness, in both Python and Java; we also provide a simplified "Learning Machine" API in those languages.
Our approach enables teaching algorithmic fairness and predictive modelling more generally very early in the students' computing career. A companion website with all our teaching materials is available at https://PredictiveModellingEarly.github.io/.
References
[1]
Julia Angwin and Jeff Larson. 2016. Machine bias: There's software used across the country to predict future criminals and it's biased against blacks. ProPublica (2016). https://www.propublica.org/article/machine-bias-risk-assessments-in-criminal-sentencing
[2]
Alexandra Chouldechova. 2017. Fair prediction with disparate impact: A study of bias in recidivism prediction instruments. Big Data, Vol. 5, 2 (2017), 153--163.
[3]
Sam Corbett-Davies and Sharad Goel. 2018. The measure and mismeasure of fairness: A critical review of fair machine learning. arXiv preprint arXiv:1808.00023 (2018).
[4]
Julia Dressel and Hany Farid. 2018. The accuracy, fairness, and limits of predicting recidivism. Science Advances, Vol. 4, 1 (2018).
Index Terms
- Auditing the COMPAS Recidivism Risk Assessment Tool: Predictive Modelling and Algorithmic Fairness in CS1
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Published In
June 2020
615 pages
ISBN:9781450368742
DOI:10.1145/3341525
- General Chairs:
- Michail Giannakos,
- Guttorm Sindre,
- Program Chairs:
- Andrew Luxton-Reilly,
- Monica Divitini
Copyright © 2020 Owner/Author.
Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.
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Association for Computing Machinery
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Published: 15 June 2020
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ITiCSE '20
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ITiCSE '20: Innovation and Technology in Computer Science Education
June 15 - 19, 2020
Trondheim, Norway
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Overall Acceptance Rate 552 of 1,613 submissions, 34%
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