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The Political Economy of AI: Towards Democratic Control of the Means of Prediction

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  • Kasy, Maximilian

Abstract

This chapter discusses the regulation of artificial intelligence (AI) from the vantage point of political economy, based on the following premises: (i) AI systems maximize a single, measurable objective. (ii) In society, different individuals have different objectives. AI systems generate winners and losers. (iii) Society-level assessments of AI require trading off individual gains and losses. (iv) AI requires democratic control of algorithms, data, and computational infrastructure, to align algorithm objectives and social welfare. I address several debates regarding the ethics and social impact of AI, including (i) fairness, discrimination, and inequality, (ii) privacy, data property rights, and data governance, (iii) value alignment and the impending robot apocalypse, (iv) explainability and accountability for automated decision-making, and (v) automation and the impact of AI on the labor market and on wage inequality. (Stone Center on Socio-Economic Inequality Working Paper)

Suggested Citation

  • Kasy, Maximilian, 2023. "The Political Economy of AI: Towards Democratic Control of the Means of Prediction," SocArXiv x7pcy, Center for Open Science.
    • Handle: RePEc:osf:socarx:x7pcy
    DOI: 10.31219/osf.io/x7pcy
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    1. Susan Athey & Raj Chetty & Guido W. Imbens & Hyunseung Kang, 2019. "The Surrogate Index: Combining Short-Term Proxies to Estimate Long-Term Treatment Effects More Rapidly and Precisely," NBER Working Papers 26463, National Bureau of Economic Research, Inc.
    2. Toru Kitagawa & Aleksey Tetenov, 2018. "Who Should Be Treated? Empirical Welfare Maximization Methods for Treatment Choice," Econometrica, Econometric Society, vol. 86(2), pages 591-616, March.
    3. Susan Athey & Stefan Wager, 2021. "Policy Learning With Observational Data," Econometrica, Econometric Society, vol. 89(1), pages 133-161, January.
    4. David H. Autor & David Dorn, 2013. "The Growth of Low-Skill Service Jobs and the Polarization of the US Labor Market," American Economic Review, American Economic Association, vol. 103(5), pages 1553-1597, August.
    5. Train,Kenneth E., 2009. "Discrete Choice Methods with Simulation," Cambridge Books, Cambridge University Press, number 9780521766555.
    6. Daron Acemoglu & Ali Makhdoumi & Azarakhsh Malekian & Asu Ozdaglar, 2022. "Too Much Data: Prices and Inefficiencies in Data Markets," American Economic Journal: Microeconomics, American Economic Association, vol. 14(4), pages 218-256, November.
    7. Emmanuel Saez & Stefanie Stantcheva, 2016. "Generalized Social Marginal Welfare Weights for Optimal Tax Theory," American Economic Review, American Economic Association, vol. 106(1), pages 24-45, January.
    8. John Knowles & Nicola Persico & Petra Todd, 2001. "Racial Bias in Motor Vehicle Searches: Theory and Evidence," Journal of Political Economy, University of Chicago Press, vol. 109(1), pages 203-232, February.
    9. Holmstrom, Bengt & Milgrom, Paul, 1991. "Multitask Principal-Agent Analyses: Incentive Contracts, Asset Ownership, and Job Design," The Journal of Law, Economics, and Organization, Oxford University Press, vol. 7(0), pages 24-52, Special I.
    10. Mas-Colell, Andreu & Whinston, Michael D. & Green, Jerry R., 1995. "Microeconomic Theory," OUP Catalogue, Oxford University Press, number 9780195102680.
    11. Sen, Amartya, 1995. "Inequality Reexamined," OUP Catalogue, Oxford University Press, number 9780198289289.
    12. Imbens,Guido W. & Rubin,Donald B., 2015. "Causal Inference for Statistics, Social, and Biomedical Sciences," Cambridge Books, Cambridge University Press, number 9780521885881.
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    Cited by:

    1. Burdin, Gabriel & Dughera, Stefano & Landini, Fabio & Belloc, Filippo, 2023. "Contested Transparency: Digital Monitoring Technologies and Worker Voice," GLO Discussion Paper Series 1340, Global Labor Organization (GLO).

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