Yashasvi,
Vineet Kumar Kankerwal,
Priyam Gupta,
Vidhi Khanduja,
Abstract
Dark patterns are deceptive design elements that influence user behaviour online, frequently with unexpected results that go beyond personal experiences. These deceptive methods unintentionally encourage excessive consumption, which can seriously impede sustainability initiatives. This paper presents Intelliguard Webscan, a system created specially to identify and combat these dishonest strategies. By means of painstaking examination and assessment of heterogeneous datasets and rigorous experimentation with multiple algorithms, among them a Support Vector Classifier (SVC), we were able to detect dark patterns with an astounding 93.22% accuracy. We propose a framework that uses an AI-powered browser plugin with a user-friendly interface to find dark patterns in e-commerce websites. It scans the current webpage in real time and warns the user about the presence of Dark Patterns. Our methodology is designed to identify typical dark patterns that are present on e-commerce websites, such as social proof, obstruction, sneaking, scarcity, false urgency, forced action, and misdirection. Our ultimate objective is to provide users with the information and resources they need to navigate the online environment confidently and wisely, ultimately promoting a responsible online environment that gives priority to mindful consumption and is in line with long-term sustainability objectives.
Keywords: Dark Patterns, Support Vector Classifier, UI/UX, Intelliguard Webscan, e-commerce websites
[This article belongs to E-Commerce for Future & Trends(ecft)]
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E-Commerce for Future & Trends
| Volume | 11 |
| Issue | 03 |
| Received | June 18, 2024 |
| Accepted | July 8, 2024 |
| Published | August 27, 2024 |