IIT BHU Hosts Third Round of Dark Patterns Buster Hackathon with 500 Participants

New Delhi: The Consumer Affairs Department at IIT BHU has successfully organised the third round of the Dark Patterns Buster Hackathon (DPBH-2023) inter-college competition. Secretary DoCA Rohit Kumar Singh attended the event as Chief Guest.

Over 500 students, 150 subject experts, and jury members participated in the final round of DPBH 2023. Participants presented their practical working models to identify 13 different types of illegal dark patterns recognised by the Union Government. 

The Dark Patterns Buster Hackathon 2023 aims to provide innovation in designing applications and software-based solutions to detect dark patterns in e-commerce platforms like browser extensions, plugins, add-ons, and mobile applications. 

Key Highlights of Dark Patterns Buster Hackathon 2023

DPBH-2023 was conducted on October 26, 2023, and received participation from more than 150 colleges and over 40,000 students. In the intra-college competition's second round, 380 teams competed, with 172 teams shortlisted for round 3. The solutions presented by these teams were to address the 13 identified dark patterns.

Secretary DoCA Rohit Singh praised the participants, stating that their solutions would aid in detecting dark patterns. He added that it could also be a significant game-changer in regulating other deceptive online practices such as fake reviews, online traps, and misleading advertisements.

Projects Presented in DPBH 2023 Round 3

In the round 3, some of the projects presented by the candidates are as follows:

• Large language models for dark pattern detection: It proposed a browser extension to detect dark patterns on e-commerce sites.

• Web and mobile platform solutions: Utilising random forest classifiers and Google's Vision API, some solutions focus on discovering "hidden information" through a user-friendly platform developed with Flask and react native.

• Serverless architecture for real-time analysis: Solutions for serverless setup using AWS Lambda and cross-encoder machine learning models, with Transformer.js to enable multi-lingual analysis, showcasing scalability and efficiency in identifying "misdirection" and "forced action."

• Modular approach for comprehensive protection: Projects use text and visual analysis with You Only Look Once (YOLO) technology and react to real-time use alerts to detect a wide range of dark patterns and prioritise user privacy.

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