Week 6: Amplifying Inequality

Instructions

This week’s focus is on algorithmic decision-making in bureaucracies. We look at how algorithmic decision making has the potential to amplify existing inequalities and, therefore, hurt vulnerable minorities. To prepare for this meeting, skim the essay by Maciejewski and then read the case studies of Virginia Eubanks’ book. Why are these algorithms being deployed by bureaucrats? Could AlgorithmWatch’s Impact Assessment Tool prevent it?

Required readings

• Eubanks, V. Automating Inequality: How High-Tech Tools Profile, Police, and Punish the Poor. New York: St. Martin’s Press, 2018. Focus on Chapter 2-3. PDF on Moodle

• AlgorithmWatch. 2021. “Automated Decision-Making Systems in the Public Sector. An Impact Assessment Tool for Public Authorities”

Further reading (General)

• Maciejewski, M. “To Do More, Better, Faster and More Cheaply: Using Big Data in Public Administration”. In: International Review of Administrative Sciences 83.1S (2017), pp. 120-135. DOI: 10.1177/0020852316640058.
• Pencheva, I., M. Esteve, and S. J. Mikhaylov. “Big Data and AI-A transformational shift for government: So, what next for research?” In: Public Policy and Administration 35.1 (2020), pp. 24-44. DOI: 10.1177/095207671878053.
• Bovens, M. and S. Zouridis. “From Street-Level to System-Level Bureaucracies: How Information and Communication Technology is Transforming Administrative Discretion and Constitutional Control”. In: Public Administration Review 62.2 (2002), pp. 174-184.
• Bell, B. W. “Replacing Bureaucrats with Automated Sorcerers?”. In: Daedalus 150.3 (2021), pp. 89-103. DOI: 10.1162/daed_a_01861.

Additional Case Studies

• Machine Bias: There’s Software Used Across the Country to Predict Future Criminals. And it’s Biased Against Blacks, ProPublica, May 23, 2016

• Everything that went wrong with the botched A-Levels algorithm, Wired, August 2020 and Ofqual’s A-level algorithm: why did it fail to make the grade?, The Guardian, August 2020

• Dressel, J. and H. Farid. “The Accuracy, Fairness, and Limits of Predicting Recidivism”. In: Science Advances 4.1 (2018), p. eaao5580. DOI: 10.1126/sciadv.aao5580.

• Obermeyer, Z., B. Powers, C. Vogeli, et al. “Dissecting Racial Bias in an Algorithm Used to Manage the Health of Populations”. In: Science 366.6464 (2019), pp. 447-453. DOI: 10.1530/ey.17.12.7.

• Byrne, T., S. Metraux, M. Moreno, et al. “Los Angeles County’s Enterprise Linkages Project: An Example of the Use of Integrated Data Systems in Making Data-Driven Policy and Program Decisions”. In: California Journal of Politics and Policy 4.2 (2012). DOI: 10.1515/cjpp-2012-0005.

• Lopez, P. “Reinforcing Intersectional Inequality via the AMS Algorithm in Austria”. In: Conference Proceedings of the th STS Conference (Critical Issues in Science, Technology and Society Studies). Ed. by G. Getzinger. Verlag der Technischen Universität Graz, 2019, pp. 289-309. DOI: 10.3217/978-3-85125-668-0-16.

• Rinta-Kahila, T., I. Someh, N. Gillespie, et al. “Algorithmic Decision-Making and System Destructiveness: A Case of Automatic Debt Recovery”. In: European Journal of Information Systems 31.3 (2022), pp. 313-338. DOI: 10.1080/0960085X.2021.1960905.

Suggested Media

• Coded Bias Documentary film avialable in UCL Mediacentral

Acknowledgments

Photo credit: https://unsplash.com/photos/1k3vsv7iIIc.