Cookie banners, the pop ups that appear to collect your consent for data collection, are a tempting ground for dark patterns. Dark patterns are design elements that are used to influence the user's choice towards an option that is not in their interest. The use of dark patterns renders consent elicitation meaningless and voids the attempts to improve a fair collection and use of data. Can machine learning be used to automatically detect the presence of dark patterns in cookie banners? In this work, a dataset of cookie banners of 300 news websites was used to train a prediction model that does exactly that. The machine learning pipeline we used includes feature engineering, parameter search, training a Gradient Boosted Tree classifier and evaluation. The accuracy of the trained model is promising, but allows a lot of room for improvement. We provide an in-depth analysis of the interdisciplinary challenges that automated dark pattern detection poses to artificial intelligence. The dataset and all the code created using machine learning is available at the url to repository removed for review.

该研究利用 300 个新闻网站的 Cookie Banner 数据集，训练出一个自动检测强制同意数据收集的恶意模式的机器学习模型，但其准确性还有很大提高空间，并探讨了自动检测恶意模式对人工智能领域的跨学科挑战。

自动检测cookie横幅中的暗模式：如何做得不好，以及为什么很难用其他方式做到