In the ever-evolving landscape of E-Commerce, dark patterns are posing a huge risk to user experiences. This project targets the identification and mitigation of two prevalent dark patterns across various e-commerce platforms including Amazon, Flipkart, IndiaMart, Alibaba, and Meesho.
- Misleading Product Information: Deceptive pricing strategies, specifically the false inflation of prices followed by presenting a seemingly substantial discount, mislead consumers. Our focus lies in recognizing this pattern of misleading crucial information.
- Dark Pattern in User Ratings: The misuse of rating systems leads to misconceptions, where a high number of 5-star ratings might not truly reflect a product's quality compared to a larger set of moderately rated reviews.
Our proposed solution hinges on the development of a robust Machine Learning model. This model is trained on a comprehensive dataset derived from high-level scraping of Amazon, encompassing crucial features such as product ID, name, original and discounted prices, ratings, and more.
The Machine Learning model functions as a detector, analyzing a product and its metadata to identify potential instances of dark patterns. When triggered, the model flags or highlights such products, signalling potential deceptive practices.
Initially conceptualized as an interactive website prototype, this solution is scalable for potential expansion as a browser extension or a standalone platform. By harnessing a vast dataset and implementing cutting-edge Machine Learning techniques, we aim to empower consumers with an intuitive tool capable of unveiling deceitful practices in product presentation and ratings across multiple e-commerce platforms.
The project's key components involve: • Data Collection and Preparation: Scraping and aggregating vast volumes of product data from Amazon, structuring it into a suitable format for machine learning model ingestion. • Feature Engineering: Extracting and refining essential features such as price differentials, rating distributions, and other indicative parameters of dark patterns. • Machine Learning Model Development: Crafting a sophisticated model that learns from the dataset to discern patterns of deceit, thereby enabling the detection of potential dark patterns. • Prototyping and Accessibility: The initial implementation takes the form of an interactive website, ensuring accessibility and usability. Future iterations may extend to browser extensions or standalone applications for broader user reach.
By pioneering this solution, we aim to revolutionize the consumer experience by arming them with the ability to discern genuine product offerings from deceptive practices prevalent in the e-commerce sphere. The project's success will be gauged through extensive testing, validation, and user feedback, emphasizing its efficacy in exposing and mitigating dark patterns, ultimately fostering a more transparent and trustworthy e-commerce environment.