Journal of Consumer Policy

, Volume 40, Issue 3, pp 367–388 | Cite as

The Empire Strikes Back: Digital Control of Unfair Terms of Online Services

  • Hans-W. Micklitz
  • Przemysław Pałka
  • Yannis Panagis
Original Paper


The authors argue that it is possible to partly automate the process of abstract control of fairness of clauses in online consumer contracts. The authors present a theoretical and empirical argument for this claim, including a brief presentation of the software they have designed. This type of automation would not replace human lawyers but would assist them and make their work more effective and efficient. Policy makers should direct their attention to the potential of using algorithmic techniques in enforcing the law regarding unfair contractual terms, and to facilitating research on and ultimately implementing such technologies.


Unfair terms Digital Enforcement Automation Machine learning 



The authors would like to thank Zeppelin University, Forschungszentrum Verbraucher, Markt, Politik/CCMP (Director Lucia Reisch), for their funding and support, both of which made this research possible.


  1. Aletras, N., Tsarapatsanis, D., Preoţiuc-Pietro, D., & Lampos, V. (2016). Predicting judicial decisions of the European court of human rights: A natural language processing perspective. PeerJ Computer Science, 2, e93.Google Scholar
  2. Alpaydin, E. (2014). Introduction to machine learning (3rd ed.). Cambridge: MIT Press.Google Scholar
  3. Alpaydin, E. (2016). Machine learning. Cambridge: MIT Press.Google Scholar
  4. Benkler, Y. (2006). The wealth of networks: How social production transforms markets and freedom. New Haven: Yale University Press.Google Scholar
  5. Boer, A. (2009). Legal theory, sources of law, and the semantic web. Amsterdam: IOS Press.Google Scholar
  6. Branting, L. K. (2017). Data-centric and logic-based models for automated legal problem solving. Artificial Intelligence and Law, 25(1), 5–27.Google Scholar
  7. Brownsword, R. (2008). Rights, regulation, and the technological revolution. Oxford: Oxford University Press.CrossRefGoogle Scholar
  8. Brownsword, R. (2016). Technological management and the rule of law. Law, Innovation and Technology, 8(1), 100–140.Google Scholar
  9. Butterfield, A., & Ngondi, G. E. (Eds.). (2016). A dictionary of Computer Science (7th ed.). Oxford: Oxford University Press.Google Scholar
  10. Chopra, S., & White, L. F. (2011). A legal theory for autonomous artificial agents. Ann Arbor: University of Michigan Press.Google Scholar
  11. Chu-Carroll, J., Fan, J., Boguraev, B. K., Carmel, D., Sheinwald, D., & Welty, C. (2012a). Finding needles in the haystack: Search and candidate generation. IBM Journal of Research and Development, 56(3.4), 6:1–6:12.Google Scholar
  12. Chu-Carroll, J., Fan, J., Schlaefer, N., & Zadrozny, W. (2012b). Textual resource acquisition and engineering. IBM Journal of Research and Development, 56(3.4), 4:1–4:11.Google Scholar
  13. Costa-jussà, M. R., & Fonollosa, J. A. R. (2015). Latest trends in hybrid machine translation and its applications. Computer Speech & Language, 32(1), 3–10.Google Scholar
  14. De Franceschi, A. (2016). European contract law and the digital single market: The implications of the digital revolution. Cambridge: Intersentia.Google Scholar
  15. Goldsmith, J. L., & Wu, T. (2006). Who controls the Internet?: Illusions of a borderless world. Oxford: Oxford University Press.Google Scholar
  16. Gowers, T. (2002). Mathematics: A very short introduction. Oxford: Oxford University Press.Google Scholar
  17. Grundmann, S., & Kull, I. (Eds.). (2017). European contract law in the digital age. Cambridge: Interesentia.Google Scholar
  18. Han, J., Kamber, M., & Pei, J. (2011). Data mining: Concepts and techniques (3rd ed.). Haryana: Morgan Kaufmann.Google Scholar
  19. Johnson, D. R., & Post, D. (1996). Law and borders—The rise of law in cyberspace. Stanford Law Review, 48(5), 36.Google Scholar
  20. Katz, D. M., Bommarito II, M. J., & Blackman, J. (2014). Predicting the behavior of the Supreme Court of the United States: A general approach. arXiv:1407.6333 [physics]. Retrieved from
  21. Lessig, L. (1999). The law of the horse: What cyber law might teach. Harvard Law Review, 113, 501.Google Scholar
  22. Lessig, L. (2006). Code version 2.0. New York: Basic Books.Google Scholar
  23. Loos, M., & Luzak, J. (2016). Wanted: A bigger stick. On unfair terms in consumer contracts with online service providers. Journal of Consumer Policy, 39(1), 63–90.Google Scholar
  24. Marr, D. (2010). Vision. A computational investigation into the human representation and processing of visual information. Cambridge: MIT Press.CrossRefGoogle Scholar
  25. Micklitz, H.-W. (2010). Reforming European unfair terms legislation in consumer contracts. European Review of Contract Law, 6(4), 347–383.Google Scholar
  26. Micklitz, H.-W., & Kas, B. (2014). Overview of cases before the CJEU on European consumer contract law (2008–2013) – Part I. European Review of Contract Law, 10(1), 1–63.Google Scholar
  27. Micklitz, H.-W., & Reich, N. (2014). The court and sleeping beauty: The revival of the unfair contract terms Directive (UCTD). Common Market Law Review, 51(3), 771–808.Google Scholar
  28. Micklitz, H.-W., Reich, N., Rott, P., & Tonner, K. (2014). European Consumer Law (2nd ed.). Cambridge: Intersentia.Google Scholar
  29. Nebbia, P. (2007). Unfair contract terms in European law: A study in comparative and EC law. Oxford: Hart.Google Scholar
  30. Palfrey, J. G., & Gasser, U. (2008). Born digital: Understanding the first generation of digital natives. New York: Basic Books.Google Scholar
  31. Palka, P. (2017). Beyond contract law in the regulation of online platforms: Terms of service are not contracts. In S. Grundmann & I. Kull (Eds.), European contract law in the digital age. Cambridge: Interesentia.Google Scholar
  32. Russell, S. J., Stuart, J., & Norvig, P. (2014). Artificial intelligence: A modern approach (Pearson new international edition.). Cambridge: Pearson.Google Scholar
  33. Sartor, G. (2011). Legislative information and the web. In G. Sartor, M. Palmirani, E. Francesconi, & M. A. Biasiotti (Eds.), Legislative XML for the Semantic Web (pp. 11–20). Dordrecht et al.: Springer.Google Scholar
  34. Schulte-Nölke, H., Twigg-Flesner, C., & Ebers, M. (Eds.). (2008). EC consumer law compendium: The consumer acquis and its transposition in the Member State [i.e. states]. Munich: Sellier.Google Scholar
  35. Schulze, R., & Staudenmayer, D. (2016). Digital revolution: Challenges for contract law in practice. Baden-Baden: Nomos.Google Scholar
  36. Surden, H. (2014). Machine learning and law essay. Washington Law Review, 89, 87–116.Google Scholar
  37. Trottier, D., & Fuchs, C. (2015). Social media, politics and the state: Protests, revolutions, riots, crime and policing in the age of Facebook, Twitter and YouTube. New York: Routledge.Google Scholar
  38. Uzuner, Ö., Zhang, X., & Sibanda, T. (2009). Machine learning and rule-based approaches to assertion classification. Journal of the American Medical Informatics Association, 16(1), 109–115.Google Scholar
  39. Wendehorst, C. (2016). Verbraucherrelevante Problemstellungen zu Besitz- und Eigentumsverhältnissen beim Internet der Dinge. Studien und Gutachten im Auftrag des Sachverständigenrats für Verbraucherfragen Dezember 2016. Retrieved from
  40. Zittrain, J. (2008). The future of the Internet. And how to stop it. New Haven: Yale University Press.Google Scholar

Copyright information

© Springer Science+Business Media, LLC 2017

Authors and Affiliations

  • Hans-W. Micklitz
    • 1
  • Przemysław Pałka
    • 1
  • Yannis Panagis
    • 2
  1. 1.Department of LawEuropean University InstituteFlorenceItaly
  2. 2.iCourts Centre of Excellence for International CourtsUniversity of CopenhagenCopenhagenDenmark

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