In a study of 200 popular Japanese mobile apps, we discovered that most of them employed dark patterns, averaging 3.9 per app. Notably, we identified a new class of dark patterns called "Linguistic Dead-Ends," such as "Untranslation" and "Alphabet Soup." These findings emphasize the need for culturally sensitive design standards and further research on dark patterns in cross-cultural contexts.

LLM-based conversational agents like ChatGPT are being used in high-stakes domains, but this exposes users to privacy risks, including data breaches and the memorization of personal information. This study focuses on user behavior and perceptions, highlighting the need for privacy-preserving techniques and improved user awareness.

This study investigates how financial technology companies employ dark patterns to influence investors' decisions. We analyzed 26 mobile apps in Norway for stocks, funds, and cryptocurrencies, aiming to identify unethical design strategies. Most apps, to varying degrees, incorporate dark patterns. Banks prioritize user data protection, while non-bank fintechs employ more deceptive practices to manipulate user behavior and interaction.

In a digital age marked by deceptive design practices, our paper delves into the world of 'dark patterns'—strategies that extract profit, harvest data, and limit consumer choice. We recognize that the absence of universally accepted definitions across academic, legislative, and regulatory spaces hinders meaningful action. Our work aims to establish a common language by harmonizing existing taxonomies and proposing a three-level framework with standardized definitions for 64 dark pattern types. This framework empowers us to advance research and regulatory efforts, fostering transparency and ethical design across digital domains.