IDEAS home Printed from https://ideas.repec.org/a/eee/teinso/v63y2020ics0160791x19306906.html
   My bibliography  Save this article

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0160791X19306906
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.techsoc.2020.101378?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    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).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Aiello, Francesco & Albanese, Giuseppe & Piselli, Paolo, 2019. "Good value for public money? The case of R&D policy," Journal of Policy Modeling, Elsevier, vol. 41(6), pages 1057-1076.
    2. Naguib, Costanza, 2019. "Estimating the Heterogeneous Impact of the Free Movement of Persons on Relative Wage Mobility," Economics Working Paper Series 1903, University of St. Gallen, School of Economics and Political Science.
    3. Erik Heilmann & Janosch Henze & Heike Wetzel, 2021. "Machine learning in energy forecasts with an application to high frequency electricity consumption data," MAGKS Papers on Economics 202135, Philipps-Universität Marburg, Faculty of Business Administration and Economics, Department of Economics (Volkswirtschaftliche Abteilung).
    4. Resce, Giuliano & Vaquero-Piñeiro, Cristina, 2022. "Predicting agri-food quality across space: A Machine Learning model for the acknowledgment of Geographical Indications," Food Policy, Elsevier, vol. 112(C).
    5. Edward L. Glaeser & Scott Duke Kominers & Michael Luca & Nikhil Naik, 2018. "Big Data And Big Cities: The Promises And Limitations Of Improved Measures Of Urban Life," Economic Inquiry, Western Economic Association International, vol. 56(1), pages 114-137, January.
    6. Tranos, Emmanouil & Incera, Andre Carrascal & Willis, George, 2022. "Using the web to predict regional trade flows: data extraction, modelling, and validation," OSF Preprints 9bu5z, Center for Open Science.
    7. Bryan T. Kelly & Asaf Manela & Alan Moreira, 2019. "Text Selection," NBER Working Papers 26517, National Bureau of Economic Research, Inc.
    8. Shan Huang & Michael Allan Ribers & Hannes Ullrich, 2021. "The Value of Data for Prediction Policy Problems: Evidence from Antibiotic Prescribing," Discussion Papers of DIW Berlin 1939, DIW Berlin, German Institute for Economic Research.
    9. Augusto Cerqua & Marco Letta & Gabriele Pinto, 2024. "On the (Mis)Use of Machine Learning with Panel Data," Papers 2411.09218, arXiv.org.
    10. Pedro Carneiro & Sokbae Lee & Daniel Wilhelm, 2020. "Optimal data collection for randomized control trials," The Econometrics Journal, Royal Economic Society, vol. 23(1), pages 1-31.
    11. Resce, Giuliano, 2022. "The impact of political and non-political officials on the financial management of local governments," Journal of Policy Modeling, Elsevier, vol. 44(5), pages 943-962.
    12. Matthew Amengual & Janice Fine, 2017. "Co‐enforcing Labor standards: the unique contributions of state and worker organizations in Argentina and the United States," Regulation & Governance, John Wiley & Sons, vol. 11(2), pages 129-142, June.
    13. Xueying Yu, 2016. "Central–local conflicts in China’s environmental policy implementation: the case of the sloping land conversion program," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 84(1), pages 77-96, November.
    14. Delogu, Marco & Lagravinese, Raffaele & Paolini, Dimitri & Resce, Giuliano, 2024. "Predicting dropout from higher education: Evidence from Italy," Economic Modelling, Elsevier, vol. 130(C).
    15. Heller, Yuval & Tubul, Itay, 2023. "Strategies in the repeated prisoner’s dilemma: A cluster analysis," MPRA Paper 117444, University Library of Munich, Germany.
    16. Michael J. Weir & Thomas W. Sproul, 2019. "Identifying Drivers of Genetically Modified Seafood Demand: Evidence from a Choice Experiment," Sustainability, MDPI, vol. 11(14), pages 1-21, July.
    17. Konstantin Boss & Andre Groeger & Tobias Heidland & Finja Krueger & Conghan Zheng, 2023. "Forecasting Bilateral Refugee Flows with High-dimensional Data and Machine Learning Techniques," Working Papers 1387, Barcelona School of Economics.
    18. Urmat Dzhunkeev, 2022. "Forecasting Unemployment in Russia Using Machine Learning Methods," Russian Journal of Money and Finance, Bank of Russia, vol. 81(1), pages 73-87, March.
    19. Filmer,Deon P. & Nahata,Vatsal & Sabarwal,Shwetlena, 2021. "Preparation, Practice, and Beliefs : A Machine Learning Approach to Understanding Teacher Effectiveness," Policy Research Working Paper Series 9847, The World Bank.
    20. Michael A. Ribers & Hannes Ullrich, 2019. "Battling Antibiotic Resistance: Can Machine Learning Improve Prescribing?," Discussion Papers of DIW Berlin 1803, DIW Berlin, German Institute for Economic Research.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:teinso:v:63:y:2020:i:c:s0160791x19306906. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: https://www.journals.elsevier.com/technology-in-society .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.