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To ensure that users of online services understand what data are collected and how they are used in algorithmic decision-making, the European Union's General Data Protection Regulation (GDPR) specifies informed consent as a minimal requirement. For online news outlets consent is commonly elicited through interface design elements in the form of a pop-up. We have manually analyzed 300 data collection consent notices from news outlets that are built to ensure compliance with GDPR. The analysis uncovered a variety of strategies or dark patterns that circumvent the intent of GDPR by design. We further study the presence and variety of these dark patterns in these "cookie consents" and use our observations to specify the concept of dark pattern in the context of consent elicitation.

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Recent advances in artifcial intelligence (AI) have led to an increased focus on automating media production. One relevant application area for AI is using speech recognition to create subtitles and closed captions for videos. The AI methods based on machine learning are still not sufciently reliable in terms of producing perfect or acceptable subtitles. To compensate for this unreliability, AI can be used to build tools that support, rather than replace, human eforts and to create semi-automated workfows. In this paper, we present a prototype for including automated speech recognition for subtitling in an existing production-grade video editing tool. We devised an experiment with 25 participants and tested the efciency and efectiveness of this tool compared to a fully manual process. The results show that there is a signifcant increase in both efectiveness and efciency for novices in subtitling. Furthermore, the participants found the augmented process to be more demanding. We identify some usability issues and design choices that pertain to making augmented subtitling easier. CCS CONCEPTS• Computing methodologies → Machine learning; • Computer systems organization → Embedded and cyber-physical systems.

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.