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Willing to dig further on dark patterns? Here are curated resources, including hundreds of publications we analyzed in our R&D Lab, conferences, webinars and job opportunities to fight dark patterns.

Milvydas Knyzelis

This research article examines the intersection of creativity and deceptive design patterns, focusing on confirmshaming as a case study. It highlights how these manipulative practices exploit digital technologies to influence user behavior, emphasizing the need for ethical awareness in digital design. The study advocates for preserving user autonomy and calls on the design community to prioritize responsible and humane digital technology practices.

Elif Cansu Yaşar

This article explores the applicability of the AI Act to e-commerce companies using AI, and how well this aligns with the Act's objectives, considering the risks associated with AI in e-commerce. The AI Act employs a risk-based approach, and understanding its relevance to e-commerce activities is crucial for compliance. While some e-commerce practices may fall under prohibited activities, most are not classified as high-risk AI systems and thus are not fully regulated by the Act. Given the significant risks of manipulation and discrimination in e-commerce, the AI Act leaves a regulatory gap in addressing the use of AI in this sector.

Paarth Naithani

Cookies are essential to today's internet, but in the EU, the ePrivacy Directive and GDPR require prior informed consent for their use. Websites typically use cookie banners to meet this requirement. However, due to inconsistent cookie laws and guidelines across EU member states, users encounter varying cookie banners, necessitating additional cognitive effort and time to manage them. Furthermore, consent mechanisms often remain deceptive, employing dark patterns and vague language. This paper proposes standardized cookie banners to address these issues, recommending uniform timing, position, text, presentation, consent options, and methods of consenting.

Meg Jones and Paul Ohm

Privacy scholars have long criticized the broken consent and notice-and-choice system that has supported managerialism and privacy harms. This study proposes a new role for consent by reconceptualizing it as voting. It suggests replacing cookie banners with ballots and self-regulators with election monitors and government overseers. This approach aims to legitimize privacy proposals by incorporating the will of users, inviting ordinary citizens and advocates to the design table, and fostering dialogue, cooperation, and trust between companies and users.

Toi Kojima, Tomoya Aiba, Soshi Maeda, Hiromi Arai, Masakatsu Nishigaki and Tetsushi Ohki

This study examines the impact of dark patterns on users who recognize and avoid them. While these deceptive designs waste time and money, they also cause stress and frustration for users who must expend extra effort to navigate around them. Through a usability study of web pages with dark patterns, the research explores how the need to avoid these manipulative designs undermines trust in companies.

Naomi Victoria Panjaitan and Katsumi Watanabe

This study delves into the recognition and impact of deceptive patterns in Japan's digital environment, exploring how these manipulative design tactics influence user behavior and trust. By focusing on a Japanese context, the research investigates the role of demographic and psychological factors in detecting deceptive patterns and assesses their emotional and behavioral consequences on users. The findings reveal that higher awareness of deceptive patterns paradoxically does not enhance users’ ability to identify or resist them. This indicates that knowledge alone is insufficient against such manipulative designs, highlighting a critical need for actionable understanding and strategies to combat these practices.

Lorena Sánchez Chamorro, Carine Lallemand and Colin M. Gray

Manipulative and deceptive design practices are ubiquitous, impacting technology users across various domains. Certain groups, like teenagers, are particularly susceptible yet understudied. This paper characterizes teenagers’ experiences with manipulative design. Through semi-structured interviews with six teenagers aged 15 to 17, we explore their daily interactions with manipulative designs in social networks, video games, and e-commerce. Reflexive thematic analysis reveals that risk is a shared experience for teenagers, shaped by their personal and social contexts. These findings are compared with existing research on the general population's experiences with manipulative designs, emphasizing the need for further understanding and support for teenagers and other vulnerable groups.

Vibhav Singh, Niraj Kumar Vishvakarma and Vinod Kumar

This research examines the obstacles to overcoming dark patterns in e-commerce, manipulative strategies that companies use to influence consumer behavior. Employing total interpretive structural modeling (TISM) and expert insights, the study identifies major barriers like user unawareness and trust in brands. It also highlights the challenges posed by designer bias and user fatigue. The goal is to educate consumers, assist regulatory bodies, and promote ethical design standards in e-commerce.

Storbjörk, Sara

This study examines the motives behind the use of privacy dark patterns and their impact on user decision-making. Privacy dark patterns are deceptive design strategies that manipulate users into making privacy-compromising choices. By reviewing existing literature, the study identifies twelve common dark patterns and explores how the dual-process theory and privacy fatigue contribute to their effectiveness. Recent advancements in machine learning have shown promise in detecting these patterns, particularly in cookie dialogs. The enforcement of GDPR compliance remains a critical factor, relying on standardization and supervisory authorities.

Arya Ramteke, Sankalp Tembhurne, Gunesh Sonawane and Prof. Ratnmala N. Bhimanpallewar

This study investigates the detection of dark patterns in e-commerce websites, which are deceptive user interfaces designed to manipulate consumer behavior. Building on existing solutions like UIGuard and other machine learning approaches, the proposed method combines web scraping techniques with fine-tuned BERT language models. By analyzing scraped textual content, the study leverages BERT's bidirectional sentence analysis to identify and explain dark patterns, aiming to enhance consumer protection and raise awareness of these unethical practices.

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