38 citations · 63 across the 6 of their papers we have counts for
5 papers · 1 filter
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…
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…
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.…