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Deep learning and principal–agent problems of algorithmic governance: The new materialism perspective

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  • Kim, Eun-Sung

Abstract

With the advent of artificial intelligence, stakeholders and experts cede their policy decisions for human affairs to computer algorithms in algorithmic governance. However, they face a new material principal-agent problem, which occurs between computer scientists as principals and computer algorithms as agents. Drawing upon new materialism, this study investigates informational asymmetry, malfeasance, agency relationships, and solutions related to the principal-agent problem. The inscrutability of computer algorithms is central to the notion of informational asymmetry and their relational agency is related to the notion of malfeasance. The principal-agent relationship is viewed as the output of socio-material assemblages in which computer scientists strive to build trust with computer algorithms. The inscrutability of computer algorithms coupled with their performativity would make it challenging for human principals to ascertain the malfeasance of computer algorithms as agents, thereby forming the material principal-agent problem. Finally, this study recommends an incremental, precautionary, and technologically pluralist approach to cope with this problem.

Suggested Citation

  • Kim, Eun-Sung, 2020. "Deep learning and principal–agent problems of algorithmic governance: The new materialism perspective," Technology in Society, Elsevier, vol. 63(C).
    • Handle: RePEc:eee:teinso:v:63:y:2020:i:c:s0160791x19306906
    DOI: 10.1016/j.techsoc.2020.101378
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    References listed on IDEAS

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    1. Jon Kleinberg & Jens Ludwig & Sendhil Mullainathan & Ziad Obermeyer, 2015. "Prediction Policy Problems," American Economic Review, American Economic Association, vol. 105(5), pages 491-495, May.
    2. Hyeon-Woo Kang & Hang-Bong Kang, 2017. "Prediction of crime occurrence from multi-modal data using deep learning," PLOS ONE, Public Library of Science, vol. 12(4), pages 1-19, April.
    3. Sadowski, Jathan & Selinger, Evan, 2014. "Creating a taxonomic tool for technocracy and applying it to Silicon Valley," Technology in Society, Elsevier, vol. 38(C), pages 161-168.
    4. Cook, Brian J. & Wood, B. Dan, 1989. "Principal-Agent Models of Political Control of Bureaucracy," American Political Science Review, Cambridge University Press, vol. 83(3), pages 965-978, September.
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    Cited by:

    1. Borch, Christian, 2022. "Machine learning, knowledge risk, and principal-agent problems in automated trading," Technology in Society, Elsevier, vol. 68(C).
    2. Evgeny V. Popov & Viktoriya L. Simonova & Vitaly V. Cherepanov, 2022. "The principal–agent problem amid digital transformation," Upravlenets, Ural State University of Economics, vol. 13(3), pages 2-15, July.
    3. Yu-Che Chen & Michael J. Ahn & Yi-Fan Wang, 2023. "Artificial Intelligence and Public Values: Value Impacts and Governance in the Public Sector," Sustainability, MDPI, vol. 15(6), pages 1-22, March.
    4. Lijuan Wu & Shanyue Jin, 2022. "Corporate Social Responsibility and Sustainability: From a Corporate Governance Perspective," Sustainability, MDPI, vol. 14(22), pages 1-15, November.
    5. Jinjin Wang & Jiadi Yang, 2022. "Culture shaping and value realization of digital media art under Internet+," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 13(3), pages 1124-1133, December.
    6. König, Pascal D. & Wenzelburger, Georg, 2021. "The legitimacy gap of algorithmic decision-making in the public sector: Why it arises and how to address it," Technology in Society, Elsevier, vol. 67(C).
    7. Milosavljević, Miloš & Radovanović, Sandro & Delibašić, Boris, 2023. "What drives the performance of tax administrations? Evidence from selected european countries," Economic Modelling, Elsevier, vol. 121(C).
    8. Kim, Eun-Sung & Oh, Yoehan & Yun, Gi Woong, 2023. "Sociotechnical challenges to the technological accuracy of computer vision: The new materialism perspective," Technology in Society, Elsevier, vol. 75(C).

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