Detecting Dark Patterns in User Interfaces Using Logistic Regression and Bag-of-Words Representation
This work addresses the issue of unethical design practices for designers, developers, and policymakers, but it is incremental as it applies existing methods to a new domain.
The paper tackled the problem of detecting deceptive dark patterns in user interfaces by proposing a logistic regression model with bag-of-words representation, achieving high predictive performance as measured by metrics like accuracy and F1-score.
Dark patterns in user interfaces represent deceptive design practices intended to manipulate users' behavior, often leading to unintended consequences such as coerced purchases, involuntary data disclosures, or user frustration. Detecting and mitigating these dark patterns is crucial for promoting transparency, trust, and ethical design practices in digital environments. This paper proposes a novel approach for detecting dark patterns in user interfaces using logistic regression and bag-of-words representation. Our methodology involves collecting a diverse dataset of user interface text samples, preprocessing the data, extracting text features using the bag-of-words representation, training a logistic regression model, and evaluating its performance using various metrics such as accuracy, precision, recall, F1-score, and the area under the ROC curve (AUC). Experimental results demonstrate the effectiveness of the proposed approach in accurately identifying instances of dark patterns, with high predictive performance and robustness to variations in dataset composition and model parameters. The insights gained from this study contribute to the growing body of knowledge on dark patterns detection and classification, offering practical implications for designers, developers, and policymakers in promoting ethical design practices and protecting user rights in digital environments.