Jeroen OogeUtrecht University | UU · Department of Information and Computing Sciences
Jeroen Ooge
Doctor
Looking for candidates interested in a PhD about visualisations for explainable AI in education
About
19
Publications
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Introduction
I am mainly working on human-centred explainable AI: I study how visual explanations can be tailored to people's needs and how they affect human behaviour and perceptions, such as model understanding and appropriate trust. While education is my favourite application domain, I have also enjoyed conducting studies in healthcare and agrifood. Besides explainable AI, I am very interested in motivational techniques such as gamification.
Publications
Publications (19)
To make predictions and explore large datasets, healthcare is increasingly applying advanced algorithms of artificial intelligence. However, to make well‐considered and trustworthy decisions, healthcare professionals require ways to gain insights in these algorithms' outputs. One approach is visual analytics, which integrates humans in decision‐mak...
In the scope of explainable artificial intelligence, explanation techniques are heavily studied to increase trust in recommender systems. However, studies on explaining recommendations typically target adults in e-commerce or media contexts; e-learning has received less research attention. To address these limits, we investigated how explanations a...
The rise of ‘big data’ in agrifood has increased the need for decision support systems that harvest the power of artificial intelligence. While many such systems have been proposed, their uptake is limited, for example because they often lack uncertainty representations and are rarely designed in a user-centred way. We present a prototypical visual...
Researchers have widely acknowledged the potential of control mechanisms with which end-users of recommender systems can better tailor recommendations. However, few e-learning environments so far incorporate such mechanisms, for example for steering recommended exercises. In addition, studies with adolescents in this context are rare. To address th...
The rise of "big data" and artificial intelligence (AI) in countless application domains comes with tremendous opportunities, but also entails challenges concerning transparency and controllability. Well-performing AI models are often "black boxes," which means that understanding how they establish outcomes is hard or even infeasible. Researchers i...
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...
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...
Researchers have widely acknowledged the potential of control mechanisms with which end-users of recommender systems can better tailor recommendations. However, few e-learning environments so far incorporate such mechanisms, for example for steering recommended exercises. In addition, studies with adolescents in this context are rare. To address th...
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...
Decision support systems (DSSs) in agriculture are becoming increasingly popular, and have begun adopting visualisations to facilitate insights into complex data. However, DSSs for agriculture are often designed as standalone applications, which limits their flexibility and portability. They also rarely provide interactivity, visualise uncertainty...
Artificial intelligence (AI) is becoming ubiquitous in the lives of both researchers and non-researchers, but AI models often lack transparency. To make well-informed and trustworthy decisions based on these models, people require explanations that indicate how to interpret the model outcomes. This paper presents our ongoing research in explainable...
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...
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...
BACKGROUND
Type 2 Diabetes Mellitus (T2DM) is a common cause of mortality worldwide: each year, the chronic disease kills over one million people, making it the ninth leading cause of death. The growing use of smartphone applications (apps) led to focusing on mobile health, which is increasingly oriented towards self-care of T2DM. With smartphone a...
Gamification researchers deem adolescents a particularly interesting audience for tailored gamification. However, empirical validation of popular player typologies and personality trait models thus far has been limited to adults. As adolescents exhibit complex behaviours that differ from older adults, these models may need adaptation. To that end,...