Dark patterns are the subject of a surge of regulatory interest in the EU. Much new legislation in the areas of consumer law, data protection and competition law include provisions on dark patterns. Businesses use dark patterns to increase their revenue at the expense of consumers who purchase products they may not need, spend more time or give up more personal data than they would otherwise. Instead of focusing on the more normative issue of when dark patterns should be considered harmful, the chapter compares the different legal frameworks applicable to these practices and asks to what extent the increasingly fragmented EU regulatory landscape can offer effective overall protection against dark patterns. While useful complementarities may arise when parallel sets of rules target different concerns or protect different values, there are also risks of inconsistencies that may lead to either under- or overenforcement due to the fragmentation of the overall regulatory framework. The chapter submits that three needs result from the state of play and offers suggestions to improve the enforcement against dark patterns based on the current EU regulatory framework.

Use Case 2 in Chapter 5 examines the regulation of MDTs in the context of commercial advertising under the General Data Protection Regulation (GDPR), the Unfair Commercial Practices Directive (UCPD), and the Audiovisual Media Services Directive (AVSMD). An analysis under the Digital Services Act (DSA) and the Artificial Intelligence Act (AIA) will follow in Chapter 6, alongside a use case focused on political advertising. In the realm of commercial advertising, MDTs intensify long-standing concerns from consumer perspectives. The UCPD serves as a crucial reference point for related laws. Including the processing and inference of mind data in the blacklist outlined in Annex I of the UCPD would have significant implications, akin to the proposed introduction of a sui generis special category of mind data within the GDPR. Importantly, a blanket ban on the processing and inference of mind data for commercial practices under Annex I UCPD would automatically prohibit these practices under the DSA.

This introduction to the book begins with a narrative of the prevalence of gun violence in the United States and the trafficking of gun violence into Mexico and Latin America. It canvasses the current statistics of gun-related murders, suicides, mass shootings, and school shootings compiled by the CDC, the Pew Research Institute, Bureau of Alcohol, Tobacco, Firearms and Explosives, the Giffords Center, and other agencies collecting gun violence data. The introduction sets forth the themes of the book, including locating the landscape of firearms litigation in the history of mass tort litigation, the ineffectiveness of various gun regulatory initiatives, the historical immunity of the firearms industry from liability for gun harms, and the recent inroads on the ability to sue firearms defendants through the enactment of targeted consumer protection and public nuisance firearms accountability statutes. The introduction suggests the argument that the 1998 Tobacco Master Settlement Agreement demonstrated that impervious, dangerous product industries could finally be held accountable, and that the Sandy Hook Elementary School litigation marked a pivotal point in opening a pathway toward suing the firearms industry and holding it accountable for gun harms.

This chapter explores the intricate relationship between consumer protection and GenAI. Prominent tools like Bing Chat, ChatGPT4.0, Google’s Gemini (formerly known as Bard), OpenAI’s DALL·E, and Snapchat’s AI chatbot are widely recognized, and they dominate the generative AI landscape. However, numerous smaller, unbranded GenAI tools are embedded within major platforms, often going unrecognized by consumers as AI-driven technology. In particular, the focus of this chapter is the phenomenon of algorithmic consumers, whose interactions with digital tools, including GenAI, have become increasingly dynamic, engaging, and personalized. Indeed, the rise of algorithmic consumers marks a pivotal shift in consumer behaviour, which is now characterized by heightened levels of interactivity and customization.

Whether consumer law should address inequality has been approached from different perspectives in Latin America and Europe. EU consumer law has primarily relied on the interpretive benchmark of the average consumer, leading to a model of “empowerment through information” in service of maximum harmonization and economic integration. In Latin America, by contrast, statutes emphasize consumer protection based on the paradigm of the vulnerable consumer, leading to more robust substantive protection in addition to classic disclosure rules. This chapter compares the EU regime to the heterodox approach of Argentina, where courts have embraced the task of using consumer protection law to reduce inequality. Moreover, a recent Argentinean reform introduced the category of the hyper-vulnerable consumer to provide enhanced protection to consumers in a situation of aggravated vulnerability due to age, gender, physical or mental state, or social, economic, ethnic, and/or cultural circumstances. The chapter also discusses the prospect of reverse convergence (i.e., EU law converging to the Latin American mode) in view of European scholars’ growing call for the recognition of “structural vulnerabilities” in the context of the digital economy, which happens to be dominated by US companies.

The United States is the Wild West of algorithmic personalized pricing. It is practiced (and researched) extensively, possibly more than anywhere else in the world, and at the same time, it is less regulated than in many of the jurisdictions surveyed in this Handbook, most notably the European Union (EU) and China. This is not necessarily puzzling. American corporations have been the driving force behind many of the technological innovations associated with the rise and development of algorithmic personalized pricing. However, there is a long tradition in the US of opposition to regulating markets, and algorithmic personalized pricing exemplifies this approach.

Many of our pressing questions about price personalization concern its current practice and potential regulations. We could be tempted to move directly to those hard questions because many – but not all – consumers, scholars, and regulators already believe with some confidence that price personalization harms consumers or treats them unfairly. In this chapter, I pause to unpack intuitions about harm and unfairness and consider systematically what the normative problems with price personalization might be so that our understanding can inform what we look for in existing practice and what we aim to achieve with new regulations.

Medical debt is the largest form of consumer debt in collections, with $88 billion recorded on credit reports, affecting roughly one in five US households. Medical debt pushes millions into financial distress and is exacerbated by harsh collection practices to garnish wages, seize assets, place liens on homes, and reduce creditworthiness. Concerned federal and state policymakers have pursued policies to protect consumers from medical debt.

Most federal medical debt policies are forms of public law – namely administrative requirements imposed by government on health care and consumer finance entities. Nevertheless, significant gaps in the federal public law of medical debt persist, leaving an important role for states, particularly in the creation of private enforcement actions for violations of state consumer protections against medical debt. States have created both public law and private law protections, prescribing standards for financial assistance, applying to a broader range of providers, barring certain collection actions, and empowering individuals to seek private remedies for violations.

Is medical debt better addressed at the federal or state level and using the tools public law or private law? The answer is all the above: Stronger national, public-law standards to guard against medical debt are critical, but federal policy should retain a vital role for what states do well – policy innovation and greater enforcement through private remedies. Preserving a meaningful role for states and private law in consumer protection policy enhances separation of powers and serves as a check against regulatory failure by federal public law solutions and the gutting of federal administrative and statutory authority.

Personalized pricing is a form of pricing where different customers are charged different prices for the same product depending on their ability to pay, based on the information that the trader holds of a potential customer. Pricing plays a relevant role in the decision-making process by the consumers, and a firm’s performance can be determined by the ability of the business entities to execute a pricing strategy accordingly. Further, pricing also determines the quality, value, and willingness to buy. Usually the willingness of a consumer depends on transparency and fairness.

Technological developments have enabled online sellers to personalize prices of the goods and services.

As the personalization of e-commerce transactions continues to intensify, the law and policy implications of algorithmic personalized pricing (APP) should be top of mind for regulators. Price is often the single most important term of consumer transactions. APP is a form of online discriminatory pricing practice whereby suppliers set prices based on consumers’ personal information with the objective of getting as close as possible to their maximum willingness to pay. As such, APP raises issues of competition, privacy, personal data protection, contract, consumer protection, and anti-discrimination law.

This book chapter looks at the legality of APP from a Canadian perspective in competition, commercial consumer law, and personal data protection law.

AI brings risks but also opportunities for consumers. When it comes to consumer law, which traditionally focuses on protecting consumers’ autonomy and self-determination, the increased use of AI also poses major challenges. This chapter discusses both the challenges and opportunities of AI in the consumer context (Section 10.2 and 10.3) and provides a brief overview of some of the relevant consumer protection instruments in the EU legal order (Section 10.4). A case study on dark patterns illustrates the shortcomings of the current consumer protection framework more concretely (Section 10.5).

Society needs to influence and mould our expectations so AI is used for the collective good. we should be reluctant to throw away hard (and recently) won consumer rights and values on the altar of technological developments.