Jeroen Ooge

Aditya Bhattacharya, Jeroen Ooge, Gregor Stiglic et al.

Explainable artificial intelligence is increasingly used in machine learning (ML) based decision-making systems in healthcare. However, little research has compared the utility of different explanation methods in guiding healthcare experts for patient care. Moreover, it is unclear how useful, understandable, actionable and trustworthy these methods are for healthcare experts, as they often require technical ML knowledge. This paper presents an explanation dashboard that predicts the risk of diabetes onset and explains those predictions with data-centric, feature-importance, and example-based explanations. We designed an interactive dashboard to assist healthcare experts, such as nurses and physicians, in monitoring the risk of diabetes onset and recommending measures to minimize risk. We conducted a qualitative study with 11 healthcare experts and a mixed-methods study with 45 healthcare experts and 51 diabetic patients to compare the different explanation methods in our dashboard in terms of understandability, usefulness, actionability, and trust. Results indicate that our participants preferred our representation of data-centric explanations that provide local explanations with a global overview over other methods. Therefore, this paper highlights the importance of visually directive data-centric explanation method for assisting healthcare experts to gain actionable insights from patient health records. Furthermore, we share our design implications for tailoring the visual representation of different explanation methods for healthcare experts.

Ivania Donoso-Guzmán, Jeroen Ooge, Denis Parra et al.

While research on explainable AI (XAI) is booming and explanation techniques have proven promising in many application domains, standardised human-centred evaluation procedures are still missing. In addition, current evaluation procedures do not assess XAI methods holistically in the sense that they do not treat explanations' effects on humans as a complex user experience. To tackle this challenge, we propose to adapt the User-Centric Evaluation Framework used in recommender systems: we integrate explanation aspects, summarise explanation properties, indicate relations between them, and categorise metrics that measure these properties. With this comprehensive evaluation framework, we hope to contribute to the human-centred standardisation of XAI evaluation.

Haley Noh, Aarna Chowdhary, Jeroen Ooge et al.

Intelligent Tutoring Systems often grant learners shared control over skill and problem selection. Prior work suggests learners exhibit diverse task-selection strategies, such as avoiding challenge, which may interact with mastery learning systems that optimize task selection based on estimated knowledge. Algorithmic constraints on problem selection may help mitigate these effects, but testing such constraints in classrooms is costly. We propose a simulation-based framework to examine how learner task-selection strategies and system constraints shape mastery learning efficiency. Using interaction data from 261 students across two mathematical domains (equation solving and graph interpretation), we simulate strategies such as Weakness Targeting and Interleaving. We evaluate how these strategies affect overpractice as a measure of efficiency. Results show substantial variability across strategies, with risk-averse strategies producing higher levels of overpractice, especially for complex multi-step problems. Targeted system constraints significantly reduce inefficiencies for maladaptive strategies while minimally affecting already efficient strategies. These findings show how simulation grounded in student data can guide the redesign of shared-control tutoring systems before classroom deployment.

Jeroen Ooge, Katrien Verbert

People's trust in prediction models can be affected by many factors, including domain expertise like knowledge about the application domain and experience with predictive modelling. However, to what extent and why domain expertise impacts people's trust is not entirely clear. In addition, accurately measuring people's trust remains challenging. We share our results and experiences of an exploratory pilot study in which four people experienced with predictive modelling systematically explore a visual analytics system with an unknown prediction model. Through a mixed-methods approach involving Likert-type questions and a semi-structured interview, we investigate how people's trust evolves during their exploration, and we distil six themes that affect their trust in the prediction model. Our results underline the multi-faceted nature of trust, and suggest that domain expertise alone cannot fully predict people's trust perceptions.