References

Module 1

Gawronski, Q. (2019). Racial bias found in widely used health care algorithm. NBC News. https://www.nbcnews.com/news/nbcblk/racial-bias-found-widely-used-health-care-algorithm-n1076436

Goldstein, M. (2023). An AI algorithm designed for child welfare could have the potential for discrimination — and now the Justice Department is looking into it. Fortune. https://fortune.com/2023/01/31/ai-algorithm-child-welfare-pitsburgh-justice-department/

Howe, C. J., Bailey, Z. D., Raifman, J. R., & Jackson, J. W. (2022). Recommendations for Using Causal Diagrams to Study Racial Health Disparities. American journal of epidemiology, 191(12), 1981–1989. https://doi.org/10.1093/aje/kwac140

Kleinberg, J., Ludwig, J., Mullainathan, S., & Sunstein, C. R. (2018). Discrimination in the Age of Algorithms. Journal of Legal Analysis, 10, 113–174.

Muhammad, K. G. (2019). The Condemnation of Blackness: Race, Crime, and the Making of Modern Urban America, With a New Preface. Cambridge, MA: Harvard University Press.

Obermeyer, Z., Powers, B., Vogeli, C., & Mullainathan, S. (2019). Dissecting racial bias in an algorithm used to manage the health of populations. Science (New York, N.Y.), 366(6464), 447–453. https://doi.org/10.1126/science.aax2342

Pearl, J., & Mackenzie, D. (2018). The Book of Why: The New Science of Cause and Effect (1st ed.). USA: Basic Books, Inc.

Wilkinson, M., et al. (2016). The FAIR Guiding Principles for scientific data management and stewardship. Scientific data, 3, 160018. https://doi.org/10.1038/sdata.2016.18

Richter, F. et al. (2023). FAIR2: A framework for addressing discrimination bias in social data science. Editorial Universitat Politècnica de València. 327-335. https://doi.org/10.4995/CARMA2023.2023.16400

Robinson, W. R., Renson, A., & Naimi, A. I. (2020). Teaching yourself about structural racism will improve your machine learning. Biostatistics, 21(2), 339–344.

Tajo, M., et al. (2024). Could machine learning help reduce inequities in the homelessness response system? Urban Institute. https://www.urban.org/urban-wire/could-machine-learning-help-reduce-inequities-homelessness-response-system


Module 2

Shaw, R. J., Harron, K. L., Pescarini, J. M., Pinto Junior, E. P., Allik, M., Siroky, A. N., Campbell, D., Dundas, R., Ichihara, M. Y., Leyland, A. H., Barreto, M. L., & Katikireddi, S. V. (2022). Biases arising from linked administrative data for epidemiological research: a conceptual framework from registration to analyses. European journal of epidemiology, 37(12), 1215–1224. https://doi.org/10.1007/s10654-022-00934-w

Richter, F.; Nelson, E.; Coury, N.; Bruckman, L.; Knighton, S. (2023). FAIR2: A framework for addressing discrimination bias in social data science. Editorial Universitat Politècnica de València. 327-335. https://doi.org/10.4995/CARMA2023.2023.16400

Richter, F. G.-C., Nelson, E., Coury, N., & Jackson, A. (2026). FAIR2 Data Chats: Advancing Public-Interest Data Science through Participatory Research. Journal of Participatory Research Methods.(doi forthcoming) https://jprm.scholasticahq.com/article/158338-fair2-data-chats-advancing-public-interest-data-science-through-participatory-research?auth_token=BTCC3oAgDnVfvmA8uJzM


Module 3

Banks, G.C., Tonidandel, S., Dou, W. et al. (2025). How to Reduce Bias in the Life Cycle of a Data Science Project. J Bus Psychol. https://doi.org/10.1007/s10869-025-10022-x

Ceccon, M., Cornacchia, G., Pezze, D., Fabris, A., Susto, G… (2025). Underrepresentation, label bias, and proxies: Towards Data Bias Profiles for the EU AI act and beyond. Expert Systems with Applications. https://doi.org/10.1016/j.eswa.2025.128266

Mhasawade, V., D’Amour, A., & Pfohl, S. R. (2024). A Causal Perspective on Label Bias. In Proceedings of the 2024 ACM Conference on Fairness, Accountability, and Transparency (pp. 1282‑1294). ACM. https://doi.org/10.1145/3630106.3658972

Suen, L. W., Lunn, M. R., Katuzny, K., Finn, S., Duncan, L., Sevelius, J., Flentje, A., Capriotti, M. R., Lubensky, M. E., Hunt, C., Weber, S., Bibbins-Domingo, K., & Obedin-Maliver, J. (2020). What Sexual and Gender Minority People Want Researchers to Know About Sexual Orientation and Gender Identity Questions: A Qualitative Study. Archives of sexual behavior, 49(7), 2301–2318. https://doi.org/10.1007/s10508-020-01810-y

Patterson, J. G., Jabson, J. M., & Bowen, D. J. (2017). Measuring Sexual and Gender Minority Populations in Health Surveillance. LGBT health, 4(2), 82–105. https://doi.org/10.1089/lgbt.2016.0026


Module 4

Elwert, F., & Winship, C. (2014). Endogenous Selection Bias: The Problem of Conditioning on a Collider Variable. Annual review of sociology, 40, 31–53. https://doi.org/10.1146/annurev-soc-071913-043455

Hernández-Díaz, S., Schisterman, E. F., & Hernán, M. A. (2006). The birth weight “paradox” uncovered?. American journal of epidemiology, 164(11), 1115–1120. https://doi.org/10.1093/aje/kwj275

Richter, F. G. C., Coulton, C., Urban, A., & Steh, S. (2021). An Integrated Data System Lens Into Evictions and Their Effects. Housing Policy Debate, 31(3–5), 762–784. https://doi.org/10.1080/10511482.2021.1879201

Pearl, J., & Mackenzie, D. (2018). The book of why: The new science of cause and effect. Basic Books.

Knox, D., Lowe, W., & Mummolo, J. (2020). Administrative Records Mask Racially Biased Policing. American Political Science Review, 114(3), 619–637. doi:10.1017/S0003055420000039

Bui, Q., & Cox, A. (2016, July 11). Surprising new evidence shows bias in police use of force but not in shootings. The New York Times. https://www.nytimes.com/2016/07/12/upshot/surprising-new-evidence-shows-bias-in-police-use-of-force-but-not-in-shootings.html