Search Results for author:
Found 4 papers, 0 papers with code
Revisiting the Dataset Bias Problem from a Statistical Perspective
To overcome this challenge, we propose to approximate \frac{1}{p(u|b)} using a biased classifier trained with "bias amplification" losses.
Towards the new XAI: A Hypothesis-Driven Approach to Decision Support Using Evidence
Prior research on AI-assisted human decision-making has explored several different explainable AI (XAI) approaches.
Explaining Model Confidence Using Counterfactuals
In this paper, we show that counterfactual explanations of confidence scores help study participants to better understand and better trust a machine learning model's prediction.
Improving Model Understanding and Trust with Counterfactual Explanations of Model Confidence
In this paper, we show that counterfactual explanations of confidence scores help users better understand and better trust an AI model's prediction in human-subject studies.
Cannot find the paper you are looking for? You can
Submit a new open access paper.
