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.

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.

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.

"Dark patterns" are deceptive design techniques used to manipulate users into actions they might not otherwise take, such as clicking on unwanted links or making unintended purchases. This paper examines the prevalence of dark patterns in the digital world, their potential harm to users, and strategies to eliminate their use. By understanding and identifying these sneaky tactics, users can avoid unnecessary expenses and save time. Unveiling dark patterns is crucial for detecting them on websites and raising awareness, ultimately protecting users from financial loss.

In the evolving tech landscape, new regulations are impacting User Experience (UX) design. This paper examines how UX practitioners integrate legal knowledge into their work through three case studies: a junior designer in a startup, a mid-level practitioner in a corporation, and a senior consultant. The study reveals varying levels of legal awareness and integration, highlighting the need for researchers and educators to adapt UX practices to better address legal and regulatory issues.

This study reveals a gap in current User Experience (UX) assessment methods: the lack of consideration for ethical dimensions, particularly in addressing "dark patterns" in interface design. By adapting the User Experience Questionnaire (UEQ) and incorporating additional items to capture responses to unethical designs, the research examines user evaluations of social media dark patterns. Initial findings suggest that while conventional UX scores may not effectively identify unethical design, subscale measures show promise in this regard. Additionally, users demonstrate an ability to discern interfaces with addictive and coercive properties, highlighting potential pathways for enhancing UX assessment practices.

This master's thesis delves into the realm of dark patterns within the scope of EU consumer law. Examining the effectiveness of the Unfair Commercial Practices Directive (UCPD), it questions whether the current legal framework adequately safeguards consumers against these deceptive design tactics. Through a thorough analysis of cognitive biases and legal provisions, the research proposes legislative amendments to better address the manipulation caused by dark patterns. Ultimately, it advocates for a revision of the average consumer benchmark to align more closely with the realities of consumer behavior influenced by these tactics.

In an interdisciplinary workshop, experts convene to explore the challenges and potential dark sides of intelligent writing assistants, fueled by the rapid advancements in artificial intelligence. Building on the success of previous initiatives like the CHI23 workshop (The Second In2Writing Workshop), the gathering aims to engage diverse voices in the writing tools research community. Participants, including writers, educators, researchers, industry professionals, students, and enthusiasts, collaborate to understand and mitigate the risks associated with the widespread adoption of writing assistant technologies.

In a pioneering study, researchers investigate the prevalence and impact of dark patterns and deceptive designs (DPs) in Japanese online shopping interfaces. Through a user study involving 30 participants, they identify Alphabet Soup and Misleading Reference Pricing as the most deceptive, while Social Proofs, Sneaking in Items, and Untranslation are less deceptive but still impactful. The study highlights the need for further research and collaboration with industry to address the influence of DPs on user behavior.