ML bias: Algorithms in the Courtroom!
This podcast is for educational purposes. Recorded in collaboration with Professor Lauren Cipriano.Some references used: Mehrabi, N., et al. (2019). A Survey on Bias and Fairness in Machine Learning. arXiv.Org. Suresh, H., & Guttag, J. V. (2021). A Framework for Understanding Sources of Harm throughout the Machine Learning Life Cycle. Equity and Access in Algorithms, Mechanisms, and Optimization, 1–9. Northpointe, Inc. (2016). COMPAS risk scales: Demonstrating accuracy equity and predictive parity. https://www.documentcloud.org/documents/2998391-ProPublica-Commentary-Final-070616Angwin, J. et al (2016). Machine bias. ProPublica. https://www.propublica.org/article/machine-bias-risk-assessments-in-criminal-sentencingLarson, et al (2016). How we analyzed the COMPAS recidivism algorithm. ProPublica. https://www.propublica.org/article/how-we-analyzed-the-compas-recidivism-algorithmCasadei, D. (2020). Predicting prison terms and parole. Retrieved from Downtown Publications: https://www.downtownpublications.com/single-post/2020/03/24/predicting-prison-terms-and-paroleDressel, J., & Farid, H. (2018). The accuracy, fairness, and limits of predicting recidivism. Science Advances, 4(1), eaao5580. Corbett-Davies et al (2016). A computer program used for bail and sentencing decisions was labeled biased against blacks. It’s actually not that clear. The Washington Post. https://www.washingtonpost.com/news/monkey-cage/wp/2016/10/17/can-an-algorithm-be-racist-our-analysis-is-more-cautious-than-propublicas/Jackson, E., & Mendoza, C. (2020). Setting the record straight: What the COMPAS Core Risk and Need Assessment is and is not. Harvard Data Science Review. Thomas, S. (2023). The fairness fallacy: Northpointe and the COMPAS recidivism prediction algorithm (Unpublished undergraduate thesis). Institute for the Study of Human Rights, Columbia UniversityAngwin, J., . ProPublica responds to company’s critique of machine bias story. ProPublica. https://www.propublica.org/article/propublica-responds-to-companys-critique-of-machine-bias-storyFlores, A. W., et al. (2016). 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.” Federal Probation, 80(2), 38-42. https://www.uscourts.gov/sites/default/files/fed_probation_dec2016.pdfBarry-Jester, A. M., et al.(2015). The new science of sentencing. The Marshall Project. https://www.themarshallproject.org/2015/08/04/the-new-science-of-sentencingMusic sourceshttps://www.youtube.com/watch?v=OwEU8dPYCvYhttps://www.youtube.com/watch?v=aUaTCOpbjcgPs: GenAI was used for editing and proofreading the script, helping to refine the content for clarity and coherence.
