Manipulation by algorithms. Exploring the triangle of unfair commercial practice, data protection, and privacy law

Abstract

The optimisation of sales practices in consumer markets through machine learning not only harbours the potential to better match consumer preferences with products, but also risks to facilitate the exploitation of consumer weaknesses discovered via data analysis. More specifically, recent technological advances have brought us to the edge of mind‐reading technologies, which automatically analyse mental states and adapt offers accordingly, in potentially manipulative ways. This article shows that, in market contexts, the challenges of manipulation by algorithm necessitate an integrated understanding of unfair commercial practice, data protection, and privacy law. It maps the interactions between these contiguous yet distinct fields of law, and draws on economics and computer science to develop a novel framework to deal with algorithmic influence. Furthermore, it critically discusses the Commission proposals for the Digital Services Act and the Artificial Intelligence Act, and suggests to complement them with more broadly applicable measures to mitigate algorithmic manipulation.

No Consumer Is an Island—Relational Disclosure as a Regulatory Strategy to Advance Consumer Protection Against Microtargeting

“…As far as private law is concerned, the impact of tailored interactions has been scrutinized under the lens of rules on defective consent (Davola, 2021); on the other hand, when examining the role that consumer law can play in tackling the risks connected to unregulated personalization of commercial interactions, the framework laid down by the Unfair Commercial Practices Directive (UCPD) 3 and by the Directive on Consumer Rights 4 (especially in light of the innovations proposed in the New Deal for Consumers 5 and the amendments subsequently introduced by the Modernization Directive 6 ) has been of particular interest (Galli, 2021;Hacker, 2021;Laux et al, 2021;Sartor et al, 2021).…”

The Concentration-after-Personalisation Index (CAPI): Governing effects of personalisation using the example of targeted online advertising

“…Today's ad-technology can predict consumer behaviour and biases beyond what consumers themselves can be expected to know or understand (Acemoglu, 2021: 6). Consequently, consumers may be persuaded to buy products they did not intend to buy (Laux et al, 2021a: 726–29; see also Hacker, 2021; Strahilevitz, 2013; Zuiderveen Borgesius, 2015: 15, 47). Such behavioural manipulation can shift surplus from the consumer to the seller (and may distort the composition of consumption, thus creating new inefficiencies, cf.…”

“…Such personalisation of both advertising and pricing affects – amongst other normative concerns (see further Hacker, 2021: 1–2; Laux et al, 2021a: 726–29) – the welfare of consumers and sellers, or ‘producers’ (Ezrachi and Stucke, 2016a: 586). Whether or not the overall welfare effect is positive or negative depends on the relative strength of the effect for consumers and producers (Marotta et al, 2021).…”

Targeted Advertising and Consumer Protection Law in the European Union

“…353 Meeting the requirements of the GDPR and ePrivacy Directive can be seen as part of professional diligence. 354 In practice, particularly important may be publishers' reliance on IAB Europe's TCF framework that combined the consumer consent across the Internet, the case that now reached the ECJ with the request for the preliminary ruling. 355 It seems that ex-post analysis of whether certain targeted advertising practices are aggressive or against professional diligence in that they distort consumer behavior by exploiting consumer vulnerabilities will be prominent in the years following the DSA's coming into force.…”