43 citations · 124 across the 10 of their papers we have counts for
11 papers
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…
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…
Let's Get You Hired: A Job Seeker's Perspective on Multi-Agent Recruitment Systems for Explaining Hiring Decisions
Aditya Bhattacharya, Katrien Verbert
During job recruitment, traditional applicant selection methods often lack transparency. Candidates are rarely given sufficient justifications for recruiting decisions, whether the…
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…
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…
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 Directive Explanations: Crafting Explainable AI Systems for Actionable Human-AI Interactions
Aditya Bhattacharya
With Artificial Intelligence (AI) becoming ubiquitous in every application domain, the need for explanations is paramount to enhance transparency and trust among non-technical user…
EXMOS: Explanatory Model Steering Through Multifaceted Explanations and Data Configurations
Aditya Bhattacharya, Simone Stumpf, Lucija Gosak +2
Explanations in interactive machine-learning systems facilitate debugging and improving prediction models. However, the effectiveness of various global model-centric and data-centr…
Lessons Learned from EXMOS User Studies: A Technical Report Summarizing Key Takeaways from User Studies Conducted to Evaluate The EXMOS Platform
Aditya Bhattacharya, Simone Stumpf, Lucija Gosak +2
In the realm of interactive machine-learning systems, the provision of explanations serves as a vital aid in the processes of debugging and enhancing prediction models. However, th…
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…