Ethical Eyes

References

[1] Yuki Yada,Jiaying Feng,Tsuneo Matsumoto,Nao Fukushima,Hayato Yamana,"Dark patterns in e-commerce: a dataset and its baseline evaluations",2022 IEEE International Conference on Big Data (Big Data)

[2] S M Hasan Mansur,Sabiha Salma,Damilola Awofisayo,Kevin Moran,"AidUI: Toward Automated Recognition of Dark Patterns in User Interfaces",2023 IEEE/ACM 45th International Conference on Software Engineering (ICSE)

[3] Daniel Kirkman,Kami Vaniea,Daniel W. Woods,"DarkDialogs: Automated detection of 10 dark patterns on cookie dialogs",2023 IEEE 8th European Symposium on Security and Privacy (EuroS&P)

[4] Cherie Lacey,Catherine Caudwell,"Cuteness as a u2018Dark Patternu2019 in Home Robots",2019 14th ACM/IEEE International Conference on Human-Robot Interaction (HRI)

[5] Davide Maria Parrilli,Rodrigo Hernu00e1ndez-Ramu00edrez,"Re-Designing Dark Patterns to Improve Privacy",2020 IEEE International Symposium on Technology and Society (ISTAS)

[6] Elizabeth Dula,Andres Rosero,Elizabeth Phillips,"Identifying Dark Patterns in Social Robot Behavior",2023 Systems and Information Engineering Design Symposium (SIEDS)

[7] Apichaya Nimkoompai,"Risk Analysis of Encountering Dark Patterns of UX E-commerce Applications Affecting Personal Data",2022 6th International Conference on Information Technology (InCIT)

[8] Jiaying Feng,Fan Mo,Yuki Yada,Tsuneo Matsumoto,Nao Fukushima,Fukuyo Kido,Hayato Yamana,"Analysis of Dark Pattern-related Tweets from 2010",2023 IEEE 8th International Conference on Big Data Analytics (ICBDA)

[9] Pumarin Tiangpanich,Apichaya Nimkoompai,"An Analysis of Differences between Dark Pattern and Anti-Pattern to Increase Efficiency Application Design",2022 7th International Conference on Business and Industrial Research (ICBIR)

@article{1711737,
      author = {Rohit R., Pugazhenthi M., Nandhakumar K., Prince Kumar, Deepthi Nair P.},
      title = {Ethical Eyes},
      journal = {Iconic Research And Engineering Journals},
      year = {2025},
      volume = {9},
      number = {5},
      pages = {274-277},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1711737.pdf},
      abstract = {This project introduces a robust system for the automated detection of dark patterns on websites, aiming to enhance user protection and transparency in online interactions. Leveraging a Naive Bayes classifier trained on dark pattern categories such as Bait and Switch, Forced Continuity, Price Comparison Prevention, Hidden Costs, and Sneaking, the model achieves effective identification of deceptive design elements. The preprocessing of textual data involves employing the TFIDF vectorizer for feature extraction, optimizing the classifier's performance. Web scraping is facilitated through cloud scraping techniques and Beautiful Soup, enabling the extraction of relevant data for classification. The resulting model file is applied to classify scraped data, empowering users to make informed decisions while navigating online interfaces. This innovative approach addresses the ethical concerns associated with dark patterns and contributes to a safer and more transparent online environment.},
      keywords = {Dark Pattern Detection, Naive Bayes Classifier, TFIDF Vectorizer, Web Scraping, Cloud Scraper, User Protection, Transparency, Deceptive Design, Online Ethics.},
      month = {November},
  }