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The impact of data access regimes on artificial intelligence and machine learning

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Abstract

Digitization triggered a steep drop in the cost of information. The resulting data glut created a bottleneck because human cognitive capacity is unable to cope with large amounts of information. Artificial intelligence and machine learning (AI/ML) triggered a similar drop in the cost of machine-based decision-making and helps in overcoming this bottleneck. Substantial change in the relative price of resources puts pressure on ownership and access rights to these resources. This explains pressure on access rights to data. ML thrives on access to big and varied datasets. We discuss the implications of access regimes for the development of AI in its current form of ML. The economic characteristics of data (non-rivalry, economies of scale and scope) favour data aggregation in big datasets. Non-rivalry implies the need for exclusive rights in order to incentivise data production when it is costly. The balance between access and exclusion is at the centre of the debate on data regimes. We explore the economic implications of several modalities for access to data, ranging from exclusive monopolistic control to monopolistic competition and free access. Regulatory intervention may push the market beyond voluntary exchanges, either towards more openness or reduced access. This may generate private costs for firms and individuals. Society can choose to do so if the social benefits of this intervention outweigh the private costs. We briefly discuss the main EU legal instruments that are relevant for data access and ownership, including the General Data Protection Regulation (GDPR) that defines the rights of data subjects with respect to their personal data and the Database Directive (DBD) that grants ownership rights to database producers. These two instruments leave a wide legal no-man's land where data access is ruled by bilateral contracts and Technical Protection Measures that give exclusive control to de facto data holders, and by market forces that drive access, trade and pricing of data. The absence of exclusive rights might facilitate data sharing and access or it may result in a segmented data landscape where data aggregation for ML purposes is hard to achieve. It is unclear if incompletely specified ownership and access rights maximize the welfare of society and facilitate the development of AI/ML.

Suggested Citation

  • Bertin Martens, 2018. "The impact of data access regimes on artificial intelligence and machine learning," JRC Working Papers on Digital Economy 2018-09, Joint Research Centre.
    • Handle: RePEc:ipt:decwpa:201809
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    1. Dengler, Sebastian & Prüfer, Jens, 2021. "Consumers' privacy choices in the era of big data," Games and Economic Behavior, Elsevier, vol. 130(C), pages 499-520.
    2. Bertin Martens & Frank Mueller-Langer, 2018. "Access to digital car data and competition in aftersales services," JRC Working Papers on Digital Economy 2018-06, Joint Research Centre.
    3. Bertin Martens, 2013. "What does economic research tell us about cross-border e-commerce in the EU Digital Single Market?," JRC Working Papers on Digital Economy 2013-05, Joint Research Centre.
    4. Dirk Bergemann & Alessandro Bonatti, 2015. "Selling Cookies," American Economic Journal: Microeconomics, American Economic Association, vol. 7(3), pages 259-294, August.
    5. Alessandro Bonatti & Dirk Bergemann, 2012. "Markets for Data," 2012 Meeting Papers 538, Society for Economic Dynamics.
    6. Graef, Inge & Wahyuningtyas, Sih Yuliana & Valcke, Peggy, 2015. "Assessing data access issues in online platforms," Telecommunications Policy, Elsevier, vol. 39(5), pages 375-387.
    7. Jens Prüfer & Christoph Schottmüller, 2021. "Competing with Big Data," Journal of Industrial Economics, Wiley Blackwell, vol. 69(4), pages 967-1008, December.
    8. Melisande Cardona & Nestor Duch-Brown & Joseph Francois & Bertin Martens & Fan Yang, 2015. "The macro-economic impact of e-commerce in the EU Digital Single Market," JRC Working Papers on Digital Economy 2015-09, Joint Research Centre.
    9. Rosen, Sherwin, 1983. "Specialization and Human Capital," Journal of Labor Economics, University of Chicago Press, vol. 1(1), pages 43-49, January.
    10. Imanol Arrieta-Ibarra & Leonard Goff & Diego Jiménez-Hernández & Jaron Lanier & E. Glen Weyl, 2018. "Should We Treat Data as Labor? Moving beyond "Free"," AEA Papers and Proceedings, American Economic Association, vol. 108, pages 38-42, May.
    11. Dirk Bergemann & Alessandro Bonatti & Alex Smolin, 2014. "Selling Experiments: Menu Pricing of Information," Cowles Foundation Discussion Papers 1952, Cowles Foundation for Research in Economics, Yale University.
    12. Wolfgang Kerber, 2016. "A New (Intellectual) Property Right for Non-Personal Data? An Economic Analysis," MAGKS Papers on Economics 201637, Philipps-Universität Marburg, Faculty of Business Administration and Economics, Department of Economics (Volkswirtschaftliche Abteilung).
    13. Nestor Duch-Brown & Bertin Martens & Frank Mueller-Langer, 2017. "The economics of ownership, access and trade in digital data," JRC Working Papers on Digital Economy 2017-01, Joint Research Centre.
    14. Randall A. Lewis & Justin M. Rao, 2015. "The Unfavorable Economics of Measuring the Returns to Advertising," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 130(4), pages 1941-1973.
    15. Bertin Martens, 2016. "An Economic Policy Perspective on Online Platforms," JRC Working Papers on Digital Economy 2016-05, Joint Research Centre.
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    Cited by:

    1. Norbäck, Pehr-Johan & Persson, Lars, 2023. "Why Big Data Can Make Creative Destruction More Creative – But Less Destructive," Working Paper Series 1454, Research Institute of Industrial Economics.
    2. Long, Vicky & Bjuggren, Per-Olof, 2022. "Working Paper No. 355: The artificial intelligence (AI) data access regime: what are the factors affecting the access and sharing of industrial AI data?," Ratio Working Papers 355, The Ratio Institute.

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    More about this item

    Keywords

    digital data; ownership and access rights; trade in data; machine learning; artificial intelligence;
    All these keywords.

    JEL classification:

    • L00 - Industrial Organization - - General - - - General

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