Tobias A. Opsahl

Tobias A. Opsahl, Vegard Antun

Most datasets used for supervised machine learning consist of a single label per data point. However, in cases where more information than just the class label is available, would it be possible to train models more efficiently? We introduce two novel model architectures, which we call hybrid concept-based models, that train using both class labels and additional information in the dataset referred to as concepts. In order to thoroughly assess their performance, we introduce ConceptShapes, an open and flexible class of datasets with concept labels. We show that the hybrid concept-based models outperform standard computer vision models and previously proposed concept-based models with respect to accuracy, especially in sparse data settings. We also introduce an algorithm for performing adversarial concept attacks, where an image is perturbed in a way that does not change a concept-based model's concept predictions, but changes the class prediction. The existence of such adversarial examples raises questions about the interpretable qualities promised by concept-based models.

Liv Hilde Sjøflot, Tobias A. Opsahl

While companies increasingly rely on data, especially when it comes to targeted advertising, adapting content to users, selling data and training machine learning models, the collection of data raises privacy concerns. One way of collecting data is by using HTTP cookies when interacting with a website. Legal regulations require service providers to collect consent for some forms of cookie collection, which is often acquired through \emph{cookie consent banners}, but their effectiveness has been debated. This study aims to understand what influences users' experience and behaviour when managing their cookie consent, by investigating the gap between their stated privacy preferences and their actual actions. A mixed methods approach was used, collecting data from a usability test and a survey (N=20). The results showed that although participants generally want to reject cookie collection, they often end up accepting because of deceptive patterns in the cookie consent banner design. It also showed that they were more willing to consent to websites they trusted and if they expected it would improve their user experience. Although the current EU legislation states that withdrawing consent must be as easy as giving it, withdrawing consent took on average more than 20 times longer than giving it. This suggests that cookie consent banners in their current form are not ideal with respect to user autonomy, often leading users to \emph{consent by design}.

Tobias A. Opsahl

Despite recent success in natural language processing (NLP), fact verification still remains a difficult task. Due to misinformation spreading increasingly fast, attention has been directed towards automatically verifying the correctness of claims. In the domain of NLP, this is usually done by training supervised machine learning models to verify claims by utilizing evidence from trustworthy corpora. We present efficient methods for verifying claims on a dataset where the evidence is in the form of structured knowledge graphs. We use the FactKG dataset, which is constructed from the DBpedia knowledge graph extracted from Wikipedia. By simplifying the evidence retrieval process, from fine-tuned language models to simple logical retrievals, we are able to construct models that both require less computational resources and achieve better test-set accuracy.