Many tech companies exploit psychological vulnerabilities to design digital interfaces that maximize the frequency and duration of user visits. Consequently, users often report feeling dissatisfied with time spent on such services. Prior work has developed typologies of damaging design patterns (or dark patterns) that contribute to financial and privacy harms, which has helped designers to resist these patterns and policymakers to regulate them. However, there is a missing collection of similar problematic patterns that lead to attentional harms. To close this gap, the authors conducted a systematic literature review for what they call ‘attention capture damaging patterns’ (ACDPs). They analyzed 43 papers to identify their characteristics, the psychological vulnerabilities they exploit, and their impact on digital wellbeing. They propose a definition of ACDPs and identify eleven common types, from Time Fog to Infinite Scroll. The typology offers technologists and policymakers a common reference to advocate, design, and regulate against attentional harms.

“How does the end user perceive, experience, and respond to dark patterns?” This is the research question which drives this inquiry. The paper contributes to an increased awareness of the phenomenon of dark patterns by exploring how users perceive and experience them. Hence, the authors chose a qualitative research approach, with focus groups and interviews for this. Their analysis shows that participants were moderately aware of these deceptive techniques, several of which were perceived as sneaky and dishonest. They further expressed a resigned attitude toward such techniques and primarily blamed businesses for their occurrence. Users also considered their dependency on services employing these practices, thus making it difficult to avoid fully dark patterns.

In this paper, the authors examine the extent to which common UI dark patterns can be automatically recognized in modern software applications. They introduce AIDUI, a novel automated approach that uses computer vision and natural language processing techniques to recognize a set of visual and textual cues in application screenshots that signify the presence of ten unique UI dark patterns, allowing for their detection, classification, and localization. To evaluate this approach, they constructed CONTEXTDP, the current largest dataset of fully-localized UI dark patterns that spans 175 mobile and 83 web UI screenshots containing 301 dark pattern instances. Overall, this work demonstrates the plausibility of developing tools to aid developers in recognizing and appropriately rectifying deceptive UI patterns.

With the nascent rise of the voice intelligence industry, consumer engagement is evolving. The expected shift from navigating digital environments by a “click” of a mouse or a “touch” of a screen to “voice commands” has set digital platforms for a race to become leaders in voice-based services. The European Commission's inquiry into the consumer IoT sector revealed that the development of the market for general-purpose voice assistants is spearheaded by a handful of big technology companies, highlighting the concerns over the contestability and growing concentration in these markets. This article posits that voice assistants are uniquely positioned to engage in dynamically personalized steering – hypernudging – of consumers toward market outcomes. It examines hypernudging by voice assistants through the lens of abuse of dominance prohibition enshrined in article 102 TFEU, showcasing that advanced user influencing, such as hypernudging, could become a vehicle for engaging in a more subtle anticompetitive self-preferencing.

The authors present automated techniques that enable experts to identify dark patterns on a large set of websites. Using these techniques, they study shopping websites, which often use dark patterns to influence users into making more purchases or disclosing more information than they would otherwise. They examine these dark patterns for deceptive practices, and find 183 erring websites . They also uncover 22 third-party entities that offer dark patterns as a turnkey solution. Finally, they develop a taxonomy of dark pattern characteristics that describes their underlying influence and their potential harm on user decision-making. Based on these findings, they make recommendations for stakeholders including researchers and regulators to study, mitigate, and minimize the use of these patterns.

The article reviews recent work on dark patterns and demonstrates that the literature does not reflect a singular concern or consistent definition, but rather, a set of thematically related considerations. Drawing from scholarship in psychology, economics, ethics, philosophy, and law, the authors articulate a set of normative perspectives for analyzing dark patterns and their effects on individuals and society and show how future research on dark patterns can go beyond subjective criticism of user interface designs and apply empirical methods grounded in normative perspectives

The authors identify a gap in related literature regarding social networking services (SNSs). In this context, studies emphasise a lack of users’ self-determination regarding control over personal data and time spent on SNSs. They collected over 16 hours of screen recordings from Facebook’s, Instagram’s, TikTok’s, and Twitter’s mobile applications to understand how dark patterns manifest in these SNSs. For this task, they turned towards HCI experts to mitigate possible difficulties of non-expert participants in recognising dark patterns, as prior studies have noticed. Supported by the recordings, two of the authors of this paper conducted a thematic analysis based on previously described taxonomies, manually classifying the recorded material while delivering two key findings: they observed which instances occur in SNSs and identified two strategies — engaging and governing — with five dark patterns undiscovered before.

The white paper examines the ethical issues in popular apps (Android and iOS) in categories including education, gaming, communication, social and dating; used by adolescents. Additional categories of apps, including music and audio, entertainment, and movies & series, were also covered in subsequent parts of the study. Ethical issues regarding the apps were also researched on the other apps such as Twitter, Reddit, Quora, and Google. In this white paper, the author also examines ethical issues associated with four key sections, namely privacy, age- appropriateness, human-in-the-loop, and user interface. In conclusion, ethical considerations in developing and deploying apps for children and adolescents are found to be necessary and cannot be undermined, considering mobile apps' influence on them.

This summarises a roundtable on ongoing and emerging consumer risks associated with dark commercial patterns online organised as part of the 99th Session (Part 2) of the Committee on Consumer Policy (CCP) on 6 November 2020. It featured panellists from academia, consumer protection authorities, and a consumer organisation, the Norwegian Consumer Council. It begins with an overview of the main themes that emerged from the discussion, including examples and categories of dark commercial patterns and their defining attributes; evidence of their prevalence online; consumer vulnerability; and tools and approaches available to consumer protection authorities and policy makers to identify and mitigate them. It then provides details of the presentations by each of the panellists, before concluding with suggested next steps.

Current online contract practices often involve situations where parties do not understand their rights and obligations under these contracts. This article examines and discusses how complex online contracts complicate and sometimes impede people from making strategic, autonomous decisions. It also addresses how legal design approaches can shed light on complexity and foster tackling of dark patterns in online contracting so as to reduce transaction costs, increase legal quality, business sustainability, and competitive business advantage.