9 papers · 1 filter
Visual-Conversational Interface for Evidence-Based Explanation of Diabetes Risk Prediction
Reza Samimi, Aditya Bhattacharya, Lucija Gosak +2
Healthcare professionals need effective ways to use, understand, and validate AI-driven clinical decision support systems. Existing systems face two key limitations: complex visual…
A Systematic Review of User-Centred Evaluation of Explainable AI in Healthcare
Ivania Donoso-Guzmán, Kristýna Sirka KacafÃrková, Maxwell Szymanski +3
Despite promising developments in Explainable Artificial Intelligence, the practical value of XAI methods remains under-explored and insufficiently validated in real-world settings…
Importance of User Control in Data-Centric Steering for Healthcare Experts
Aditya Bhattacharya, Simone Stumpf, Katrien Verbert
As Artificial Intelligence (AI) becomes increasingly integrated into high-stakes domains like healthcare, effective collaboration between healthcare experts and AI systems is criti…
Show Me How: Benefits and Challenges of Agent-Augmented Counterfactual Explanations for Non-Expert Users
Aditya Bhattacharya, Tim Vanherwegen, Katrien Verbert
Counterfactual explanations offer actionable insights by illustrating how changes to inputs can lead to different outcomes. However, these explanations often suffer from ambiguity…
Explanatory Debiasing: Involving Domain Experts in the Data Generation Process to Mitigate Representation Bias in AI Systems
Aditya Bhattacharya, Simone Stumpf, Robin De Croon +1
Representation bias is one of the most common types of biases in artificial intelligence (AI) systems, causing AI models to perform poorly on underrepresented data segments. Althou…
Designing Visual Explanations and Learner Controls to Engage Adolescents in AI-Supported Exercise Selection
Jeroen Ooge, Arno Vanneste, Maxwell Szymanski +1
E-learning platforms that personalise content selection with AI are often criticised for lacking transparency and controllability. Researchers have therefore proposed solutions suc…