426 citations · 627 across the 19 of their papers we have counts for
17 papers · 1 filter
XAI Evaluation Cards: A Practical Method for Designing Human-Centred XAI Evaluations
Kristýna Sirka Kacafírková, Ivania Donoso-Guzmán, Denis Parra +2
Evaluating explainable AI (XAI) systems from a human-centred approach requires researchers to select from numerous evaluation dimensions and measures, often in an ad hoc and fragme…
Spotting (and Missing) Algorithmic Bias: Investigating User Understanding in a Fairness Assessment Tool
Anna Verheyden, Yizhe Zhang, Robin De Croon +2
Fairness metric selection is typically left to data scientists, but which biases are problematic and which metric captures them best depends on stakeholders' experience and domain…
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 Kacafírková, 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…
Representation Debiasing of Generated Data Involving Domain Experts
Aditya Bhattacharya, Simone Stumpf, Katrien Verbert
Biases in Artificial Intelligence (AI) or Machine Learning (ML) systems due to skewed datasets problematise the application of prediction models in practice. Representation bias is…
Petal-X: Human-Centered Visual Explanations to Improve Cardiovascular Risk Communication
Diego Rojo, Houda Lamqaddam, Lucija Gosak +1
Cardiovascular diseases (CVDs), the leading cause of death worldwide, can be prevented in most cases through behavioral interventions. Therefore, effective communication of CVD ris…
An Explanatory Model Steering System for Collaboration between Domain Experts and AI
Aditya Bhattacharya, Simone Stumpf, Katrien Verbert
With the increasing adoption of Artificial Intelligence (AI) systems in high-stake domains, such as healthcare, effective collaboration between domain experts and AI is imperative.…
Towards a Comprehensive Human-Centred Evaluation Framework for Explainable AI
Ivania Donoso-Guzmán, Jeroen Ooge, Denis Parra +1
While research on explainable AI (XAI) is booming and explanation techniques have proven promising in many application domains, standardised human-centred evaluation procedures are…
Steering Recommendations and Visualising Its Impact: Effects on Adolescents' Trust in E-Learning Platforms
Jeroen Ooge, Leen Dereu, Katrien Verbert
Researchers have widely acknowledged the potential of control mechanisms with which end-users of recommender systems can better tailor recommendations. However, few e-learning envi…
Directive Explanations for Monitoring the Risk of Diabetes Onset: Introducing Directive Data-Centric Explanations and Combinations to Support What-If Explorations
Aditya Bhattacharya, Jeroen Ooge, Gregor Stiglic +1
Explainable artificial intelligence is increasingly used in machine learning (ML) based decision-making systems in healthcare. However, little research has compared the utility of…
Trust in Prediction Models: a Mixed-Methods Pilot Study on the Impact of Domain Expertise
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…
AHMoSe: A Knowledge-Based Visual Support System for Selecting Regression Machine Learning Models
Diego Rojo, Nyi Nyi Htun, Denis Parra +2
Decision support systems have become increasingly popular in the domain of agriculture. With the development of automated machine learning, agricultural experts are now able to tra…
Middle-Aged Video Consumers' Beliefs About Algorithmic Recommendations on YouTube
Oscar Alvarado, Hendrik Heuer, Vero Vanden Abeele +2
User beliefs about algorithmic systems are constantly co-produced through user interaction and the complex socio-technical systems that generate recommendations. Identifying these…