Artificial Intelligence, Bureaucratic Discretion, and Democratic Administration
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DOI: 10.31219/osf.io/gqcnd_v1
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- Joe Soss & Richard C. Fording & Sanford F. Schram, 2008. "The Color of Devolution: Race, Federalism, and the Politics of Social Control," American Journal of Political Science, John Wiley & Sons, vol. 52(3), pages 536-553, July.
- Sendhil Mullainathan & Jann Spiess, 2017. "Machine Learning: An Applied Econometric Approach," Journal of Economic Perspectives, American Economic Association, vol. 31(2), pages 87-106, Spring.
- Jon Kleinberg & Himabindu Lakkaraju & Jure Leskovec & Jens Ludwig & Sendhil Mullainathan, 2018.
"Human Decisions and Machine Predictions,"
The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 133(1), pages 237-293.
- Jon Kleinberg & Himabindu Lakkaraju & Jure Leskovec & Jens Ludwig & Sendhil Mullainathan, 2017. "Human Decisions and Machine Predictions," NBER Working Papers 23180, National Bureau of Economic Research, Inc.
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