AI and consumers manipulation: what the role of EU fair marketing law?

Main Article Content

Federico Galli

Resumo

As empresas online de hoje recorrem, cada vez mais, a dife-rentes ferramentas de Inteligência Artificial (IA) para executar tarefas de marketing e gestão de clientes (entre as quais a publicidade direcionada, as recomendações de produtos e a personalização de preços). Trata-se de um desenvolvimento que representa uma ameaça à autonomia dos consumidores, na medida em que aumenta a probabilidade de re-sultados manipulativos nas decisões de aquisição de bens e serviços. Este artigo elucida duas formas através das quais a utilização da IA pode conduzir a resultados distorcidos na tomada de decisões por parte dos consumidores. Além disso, são avançadas algumas reflexões sobre o papel que o direito da publicidade deve desempenhar na proteção dos consumidores, principalmente sobre como o direito da publicidade da UE deve responder à disseminação das práticas comerciais mediadas pela IA. O artigo foca-se, em particular, na análise da Diretiva sobre Práticas Comerciais Desleais e da recente proposta legislativa norte-americana para a adoção do Detour Act.

Palavras-chave: Inteligência artificial, Aprendizagem automática, Práticas comerciais, Marketing de IA, Direito do consumidor da UE, Diretiva sobre Práticas Comerciais Desleais, Detour act dos EUA

Downloads

Não há dados estatísticos.

Referências

Bagozzi, R. P., Gopinath, M., and Nyer, P. U. (1999). The role of emotions in marketing. Journal of the academy of marketing science, 27(2):184-206.

Balkin, J. M. (2015). The path of robotics law. California Law Review, Forthcoming.

Brownsword, R. (2011). Autonomy, delegation, and responsibility: Agents in autonomic computing environments. In Law, Human Agency and Autonomic Computing, pp. 80-100. Routledge.

Brownsword, R. (2016). The E-Commerce Directive, consumer transactions, and the digital single market: questions of regulatory fitness, regulatory disconnection and rule redirection. In European contract law in the digital age, pp. 163-204. Intersentia.

Burr, C. and Cristianini, N. (2019). Can machines read our minds? Minds and Machines, pp. 1-34.

Burr, C., Cristianini, N., and Ladyman, J. (2018). An analysis of the interaction between intelligent software agents and human users. Minds and machines, 28(4):735-774.

Burrell, J. (2016). How the machine ‘thinks’: Understanding opacity in machine learning algorithms. Big Data & Society. https://doi.org/10.1177/2053951715622512

Calo, R. (2014). Digital market manipulation. Geo. Wash. L. Rev., 82:995.

Cambria, E. (2016). Affective computing and sentiment analysis. IEEE Intelligent Systems, 31(2):102-107.

Culotta, A. and Cutler, J. (2016). Mining brand perceptions from twitter social networks. Marketing science, 35(3):343-362.

D’mello, S. K. and Kory, J. (2015). A review and meta-analysis of multimodal affect detection systems. ACM Computing Surveys (CSUR), 47(3):43.

Ebers, M. (2019). Chapter 2: Regulating AI and Robotics: Ethical and Legal Challenges, in Martin Ebers and Susana Navas Navarro (eds.), Algorithms and Law, Cambridge, Cambridge University Press. Available at SSRN: https://ssrn.com/abstract=3392379 or http://dx.doi.org/10.2139/ssrn.3392379

Ezrachi, A., and Stucke, M. (2016). E. Virtual Competition. Harvard University Press.

Floridi, L. (2019). Marketing as control of human interfaces and its political exploitation. Philosophy & Technology, pp. 1-10.

François-Lavet, V., Henderson, P., Islam, R., Bellemare, M. G., Pineau, J., et al., (2018). An introduction to deep reinforcement learning. Foundations and Trends in Machine Learning, 11(3-4):219-354.

Goel, S., Hofman, J. M., Lahaie, S., Pennock, D. M., and Watts, D. J. (2010). Predicting consumer behavior with web search. Proceedings of the National academy of sciences, 107(41):17486-17490.

Gray, C. M., Kou, Y., Battles, B., Hoggatt, J., and Toombs, A. L. (2018). The dark (patterns) side of UX design. In Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems, p. 534. ACM.

Hacker, P. (2017). Personal data, exploitative contracts, and algorithmic fairness: Autonomous vehicles meet the Internet of Things. International Data Privacy Law, 7(4):266–286.

Hannak, A., Soeller, G., Lazer, D., Mislove, A., and Wilson, C. (2014). Measuring price discrimination and steering on e-commerce web sites. In Proceedings of the 2014 conference on internet measurement conference (pp. 305-318). ACM.

Harambam, J., Helberger, N., and van Hoboken, J. (2018). Democratizing algorithmic news recommenders: how to materialize voice in a technologically saturated media ecosystem. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 376(2133):20180088.

Hauser, J. R., Urban, G. L., Liberali, G., and Braun, M. (2009). Website morphing. Marketing Science, 28(2):202-223.

Helberger, N. (2016). Profiling and targeting consumers in the Internet of Things – A new challenge for consumer law. In Digital revolution: challenges for contract law in practice, pp. 135-161. Nomos.

Hirsh, J. B., Kang, S. K., and Bodenhausen, G. V. (2012). Personalized persuasion: Tailoring persuasive appeals to recipients’ personality traits. Psychological science, 23(6):578-581.

Hoofnagle, C. J. (2016). Federal Trade Commission privacy law and policy. Cambridge University Press.

Howells, G., Micklitz, H.-W., and Wilhelmsson, T. (2006). European fair-trading law: The unfair commercial practices directive. Routledge.

Howells, G., Twigg-Flesner, C., and Wilhelmsson, T. (2017). Rethinking EU consumer law. Routledge.

Hu, A. and Flaxman, S. (2018). Multimodal sentiment analysis to explore the structure of emotions. In Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 350-358. ACM.

Incardona, R. and Poncibo, C. (2007). The average consumer, the unfair commercial practices directive, and the cognitive revolution. Journal of consumer policy, 30(1):21-38.

Jabłonowska, A., Kuziemski, M., Nowak, A. M., Micklitz, H.-W., Pałka, P., and Sartor, G. (2018). Consumer law and Artificial Intelligence: Challenges to the EU consumer law and policy stemming from the business’ use of Artificial Intelligence – Final report of the ARTSY project.

Kahneman, D. (2011). Thinking, fast and slow. Macmillan.

Kaptein, M., Markopoulos, P., De Ruyter, B., and Aarts, E. (2015). Personalizing persuasive technologies: Explicit and implicit personalization using persuasion profiles. International Journal of Human-Computer Studies, 77:38-51.

Keirsbilck, B. (2011). The new European law of unfair commercial practices and competition law. Hart Oxford.

Kelleher, J. D., Mac Namee, B., and D’arcy, A. (2015). Fundamentals of machine learning for predictive data analytics: algorithms, worked examples, and case studies. MIT Press.

Kosinski, M., Stillwell, D., and Graepel, T. (2013). Private traits and attributes are predictable from digital records of human behavior. Proceedings of the National Academy of Sciences, 110(15):5802-5805.

Matz, S. C., Gladstone, J. J., and Stillwell, D. J. (2016). Money buys happiness when spending fits our personality. Psychological science, 27(5):715-725.

Matz, S. C., Kosinski, M., Nave, G., and Stillwell, D. J. (2017). Psychological targeting as an effective approach to digital mass persuasion. Proceedings of the national academy of sciences, 114(48):12714-12719.

Mazzini, G. (2019). A System of Governance for Artificial Intelligence through the Lens of Emerging Intersections between AI and EU Law, in A. De Franceschi – R. Schulze (eds.), Digital Revolution – New challenges for Law. Available at SSRN: https://ssrn.com/abstract=3369266

Mik, E. (2016). The erosion of autonomy in online consumer transactions. Law, Innovation and Technology, 8(1):1-38.

Mirsch, T., Lehrer, C., and Jung, R. (2017). Digital nudging: Altering user behavior in digital environments. Proceedings der 13. Internationalen Tagung Wirtschaftsinformatik (WI 2017), 634-648.

Ning, H., Dhelim, S., and Aung, N. (2019). PersoNet: Friend Recommendation System Based on Big-Five Personality Traits and Hybrid Filtering, in IEEE Transactions on Computational Social Systems, vol. 6, no. 3, pp. 394-402. https://doi.org/10.1109/TCSS.2019.2903857.

Odekerken-Schröder, G., De Wulf, K., and Schumacher, P. (2003). Strengthening outcomes of retailer-consumer relationships: The dual impact of relationship marketing tactics and consumer personality. Journal of business research, 56(3):177-190.

Quercia, D., Kosinski, M., Stillwell, D., and Crowcroft, J. (2011). Our twitter profiles, our selves: Predicting personality with twitter. In 2011 IEEE third international conference on privacy, security, risk and trust and 2011 IEEE third international conference on social computing, pp. 180-185. IEEE.

Richardson, M., Dominowska, E., and Ragno, R. (2007). Predicting clicks: estimating the click-through rate for new ads. In Proceedings of the 16th international conference on World Wide Web, pages 521-530. ACM.

Roffo, G. and Vinciarelli, A. (2016). Personality in computational advertising: A benchmark.

Sax, M., Helberger, N., and Bol, N. (2018). Health as a means towards profitable ends: mHealth apps, user autonomy, and unfair commercial practices. Journal of consumer policy, 41(2):103-134.

Schwartz, H. A., Sap, M., Kern, M. L., Eichstaedt, J. C., Kapelner, A., Agrawal,

M., Blanco, E., Dziurzynski, L., Park, G., Stillwell, D., et al. (2016). Predicting individual well-being through the language of social media. In Biocomputing 2016: Proceedings of the Pacific Symposium, pp. 516-527. World Scientific.

Sibony, A.-L. (2014). Can EU consumer law benefit from behavioural insights? An analysis of the unfair practices directive. European Review of Private Law, 22(6):901-941.

Stuyck, J., Terryn, E., and Van Dyck, T. (2006). Confidence through fairness? The new Directive on unfair business-to-consumer commercial practices in the internal market. Common market law review, 43(1):107-152.

Sunstein, C. R. (2016). The ethics of influence: Government in the age of behavioral science. Cambridge University Press.

Teixeira, T., Wedel, M., and Pieters, R. (2012). Emotion-induced engagement in internet video advertisements. Journal of marketing research, 49(2):144-159.

Thaler, R. H. and Sunstein, C. R. (2009). Nudge: Improving decisions about health, wealth, and happiness. Penguin.

Trzaskowski, J. (2018). Behavioural innovations in marketing law. In Research Methods in Consumer Law. Edward Elgar Publishing.

Tucker, J. A., Guess, A., Barberá, P., Vaccari, C., Siegel, A., Sanovich, S., Stukal, D., and Nyhan, B. (2018). Social media, political polarization, and political disinformation: A review of the scientific literature. Political Polarization, and Political Disinformation: A Review of the Scientific Literature (March 19, 2018).

Twigg-Flesner, C. (2018). The EU’s Proposals for regulating B2B relationships on online platforms transparency, fairness and beyond. Journal of European Consumer and Market Law, 7(6):222–233.

Varian, H. (2018). Artificial intelligence, economics, and industrial organization. Technical report, National Bureau of Economic Research.

Varian, H. R. (2010). Computer mediated transactions. American Economic Review, 100(2):1-10.

Waddington, L. (2013). Vulnerable and confused: the protection of “vulnerable” consumers under EU law. European law review, 38(6):757-782.

Weatherill, S. (2013). EU consumer law and policy. Edward Elgar Publishing.

Yarkoni, T. (2010). Personality in 100,000 words: A large-scale analysis of personality and word use among bloggers. Journal of research in personality, 44(3):363-373.

Zuboff, S. (2015). Big other: surveillance capitalism and the prospects of an information civilization. Journal of Information Technology, 30(1):75-89.

Zuboff, S. (2019). The age of surveillance capitalism: The fight for a human future at the new frontier of power. Profile Books.