论文标题
曲奇横幅中对黑色图案的自动检测：如何做得不好，为什么很难做任何其他方式
Automated detection of dark patterns in cookie banners: how to do it poorly and why it is hard to do it any other way
论文作者
论文摘要
Cookie横幅，即似乎收集您同意数据收集的弹出式UPS，是黑暗图案的诱人基础。黑暗模式是设计元素，用于影响用户选择的选择，而不是符合其利益的选择。黑暗模式的使用使同意同意启发毫无意义，并使尝试改善数据收集和使用的尝试无意义。是否可以使用机器学习来自动检测饼干横幅中的黑色图案的存在？在这项工作中，使用了300个新闻网站的Cookie横幅数据集来训练一个完全实现这一目标的预测模型。我们使用的机器学习管道包括功能工程，参数搜索，培训梯度提升的树分类器和评估。训练有素的模型的准确性是有希望的，但有很大的改进空间。我们对自动化的黑暗模式检测对人工智能的构成的跨学科挑战提供了深入的分析。数据集和使用机器学习创建的所有代码可在URL上获得以删除存储库以进行审查。
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.
