Dark patterns manipulate user choices through deceptive UI tactics. Any automated detection technique for dark patterns must address the varying nature of dark patterns. Existing detection techniques need manual intervention in some cases. In other cases, techniques are not generalized due to overfitting problems; for example, these techniques can not handle cases where texts are semantically similar but possess lexical differences. We propose an automated dark pattern text detection technique that is generalized. We synthesize inclusive definitions of dark pattern categories. This contextual information is prioritized using in-context learning capabilities of GPT-3 to detect and classify dark pattern texts. Results show that our technique offers satisfactory performance for 6 out of 7 dark pattern categories explored in this study. We also validate the improved generalization capability of our technique by outperforming an existing baseline model on a test dataset.
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Automated Detection of Dark Patterns Using In-Context Learning Capabilities of GPT-3
This research proposes an automated, generalized dark pattern text detection technique using GPT-3’s in-context learning, demonstrating satisfactory performance for most dark pattern categories and outperforming existing baseline models.