IDEAS home Printed from https://ideas.repec.org/p/aoz/wpaper/232.html
   My bibliography  Save this paper

Big Data, Algorithms, AI, Ethics, and the Economy: An Aristotelian Perspective

Author

Listed:
  • Ricardo F. Crespo

    (Universidad Austral)

Abstract

While a growing body of literature points to the advantages of using algorithms in big data processing, as well as applying them to artificial intelligence (AI), in order to achieve a desired output, it also warns about the pitfalls and perils in algorithm decision-making. Algorithms and AI are the machines and big data is the new oil. Criticisms come from different fields: legal, social, political, medical, and the economic. They argue that algorithms have the power to predict our wishes and behavior and, subsequently, to manage our life: they decide the music we listen to, the news we read, the information we obtain, the content we see online, the movies we watch, the health care we receive, the products we buy, and so on.

Suggested Citation

  • Ricardo F. Crespo, 2023. "Big Data, Algorithms, AI, Ethics, and the Economy: An Aristotelian Perspective," Working Papers 232, Red Nacional de Investigadores en Economía (RedNIE).
    • Handle: RePEc:aoz:wpaper:232
    as

    Download full text from publisher

    File URL: https://rednie.eco.unc.edu.ar/files/DT/232.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. repec:ulb:ulbeco:2013/249805 is not listed on IDEAS
    2. Ljubica Nedelkoska & Glenda Quintini, 2018. "Automation, skills use and training," OECD Social, Employment and Migration Working Papers 202, OECD Publishing.
    Full references (including those not matched with items on IDEAS)

    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. Josten, Cecily & Lordan, Grace, 2022. "Automation and the Changing Nature of Work," IZA Discussion Papers 15180, Institute of Labor Economics (IZA).
    2. Lütkenhorst, Wilfried, 2018. "Creating wealth without labour? Emerging contours of a new techno-economic landscape," IDOS Discussion Papers 11/2018, German Institute of Development and Sustainability (IDOS).
    3. Laura Abrardi & Carlo Cambini & Laura Rondi, 2022. "Artificial intelligence, firms and consumer behavior: A survey," Journal of Economic Surveys, Wiley Blackwell, vol. 36(4), pages 969-991, September.
    4. Roberto Antonietti & Luca Cattani & Francesca Gambarotto & Giulio Pedrini, 2021. "Education, routine, and complexity-biased Knowledge Enabling Technologies: Evidence from Emilia-Romagna, Italy," Discussion Paper series in Regional Science & Economic Geography 2021-07, Gran Sasso Science Institute, Social Sciences, revised May 2021.
    5. Egana-delSol, Pablo & Bustelo, Monserrat & Ripani, Laura & Soler, Nicolas & Viollaz, Mariana, 2022. "Automation in Latin America: Are Women at Higher Risk of Losing Their Jobs?," Technological Forecasting and Social Change, Elsevier, vol. 175(C).
    6. Brunello, Giorgio & Rückert, Désirée & Weiss, Christoph & Wruuck, Patricia, 2023. "Advanced digital technologies and investment in employee training: Complements or substitutes?," EIB Working Papers 2023/01, European Investment Bank (EIB).
    7. Sandra M. Leitner & Robert Stehrer, 2019. "The Automatisation Challenge Meets the Demographic Challenge: In Need of Higher Productivity Growth," wiiw Working Papers 171, The Vienna Institute for International Economic Studies, wiiw.
    8. Santana, Monica & Cobo, Manuel J., 2020. "What is the future of work? A science mapping analysis," European Management Journal, Elsevier, vol. 38(6), pages 846-862.
    9. Goos, Maarten & Rademakers, Emilie & Röttger, Ronja, 2021. "Routine-Biased technical change: Individual-Level evidence from a plant closure," Research Policy, Elsevier, vol. 50(7).
    10. Montobbio, Fabio & Staccioli, Jacopo & Virgillito, Maria Enrica & Vivarelli, Marco, 2022. "Robots and the origin of their labour-saving impact," Technological Forecasting and Social Change, Elsevier, vol. 174(C).
    11. Pedota, Mattia & Grilli, Luca & Piscitello, Lucia, 2023. "Technology adoption and upskilling in the wake of Industry 4.0," Technological Forecasting and Social Change, Elsevier, vol. 187(C).
    12. Fernández-Macías, Enrique & Klenert, David & Antón, José-Ignacio, 2021. "Not so disruptive yet? Characteristics, distribution and determinants of robots in Europe," Structural Change and Economic Dynamics, Elsevier, vol. 58(C), pages 76-89.
    13. Damioli, Giacomo & Van Roy, Vincent & Vertesy, Daniel & Vivarelli, Marco, 2021. "Will the AI revolution be labour-friendly? Some micro evidence from the supply side," MERIT Working Papers 2021-016, United Nations University - Maastricht Economic and Social Research Institute on Innovation and Technology (MERIT).
    14. Hensvik, Lena & Skans, Oskar Nordström, 2023. "The skill-specific impact of past and projected occupational decline," Labour Economics, Elsevier, vol. 81(C).
    15. Pouliakas, Konstantinos & Branka, Jiri, 2020. "EU Jobs at Highest Risk of COVID-19 Social Distancing: Will the Pandemic Exacerbate Labour Market Divide?," IZA Discussion Papers 13281, Institute of Labor Economics (IZA).
    16. Mahdi Ghodsi & Oliver Reiter & Robert Stehrer & Roman Stöllinger, 2020. "Robotisation, Employment and Industrial Growth Intertwined Across Global Value Chains," wiiw Working Papers 177, The Vienna Institute for International Economic Studies, wiiw.
    17. Cirillo, Valeria & Evangelista, Rinaldo & Guarascio, Dario & Sostero, Matteo, 2021. "Digitalization, routineness and employment: An exploration on Italian task-based data," Research Policy, Elsevier, vol. 50(7).
    18. Klaus Gründler & Niklas Potrafke, 2023. "Population Aging, Retirement, and Aggregate Productivity," CESifo Working Paper Series 10594, CESifo.
    19. Alonso, Cristian & Berg, Andrew & Kothari, Siddharth & Papageorgiou, Chris & Rehman, Sidra, 2022. "Will the AI revolution cause a great divergence?," Journal of Monetary Economics, Elsevier, vol. 127(C), pages 18-37.
    20. Emil Mihaylov & Kea Tijdens, 2019. "Measuring the Routine and Non-Routine Task Content of 427 Four-Digit ISCO-08 Occupations," Tinbergen Institute Discussion Papers 19-035/IV, Tinbergen Institute.

    More about this item

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    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:aoz:wpaper:232. 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: Laura Inés D Amato (email available below). General contact details of provider: https://edirc.repec.org/data/redniar.html .

    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.