• Eubanks, V. (2018). Automating Inequality: How High-Tech Tools Profile, Police, and Punish the Poor . St. Martin’s Press. – A detailed investigation into algorithmic systems used by public agen- cies (for welfare, homelessness services, etc.) and how they often marginalize vulnerable groups. Eubanks provides real case studies where technology intended to improve efficiency instead created new injustices and obscured accountability, aligning with the theme of unintended consequences in public policy. • Pasquale, F. (2015). The Black Box Society: The Secret Algorithms That Control Money and Information . Harvard University Press. – A law scholar’s account of how corporate and government algo- rithms operate as “black boxes,” lacking transparency or oversight. Pasquale argues for algorithmic accountability, reinforcing the idea that without proper governance, tech-driven decision systems can become a form of theft of the public’s right to understand and in- fluence outcomes. • Sharkey, C. M., & Cuéllar, M.-F., et al. (2020). Government by Algorithm: Artificial Intelligence in Federal Administrative Agencies . Report for the Administrative Conference of the U.S. – A comprehensive study of the growing use of AI across U.S. federal agencies. It documents applications of machine learning in areas like benefits administration, regulatory monitoring, and service de- livery, and discusses the implications for transparency and admin- istrative law. The report’s findings help readers consider how new technologies might either alleviate or aggravate issues of efficiency and representation in government. 6. Infrastructure Policy, Climate Adaptation, and Public Goods Allocation • Flyvbjerg, B., Bruzelius, N., & Rothengatter, W. (2003). Megaprojects and Risk: An Anatomy of Ambition . Cambridge University Press. – Although published in 2003, this study of large
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