Uncertainty Awareness and Trust in Explainable AI- On Trust Calibration using Local and Global Explanations
This work addresses trust calibration for users of XAI systems, but it appears incremental as it builds on existing XAI concepts without introducing a major breakthrough.
The study tackled the problem of trust calibration in Explainable AI by focusing on uncertainty explanations and global explanations, which are often overlooked, and found that an algorithm providing intuitive visual understanding can enhance user satisfaction and human interpretability.
Explainable AI has become a common term in the literature, scrutinized by computer scientists and statisticians and highlighted by psychological or philosophical researchers. One major effort many researchers tackle is constructing general guidelines for XAI schemes, which we derived from our study. While some areas of XAI are well studied, we focus on uncertainty explanations and consider global explanations, which are often left out. We chose an algorithm that covers various concepts simultaneously, such as uncertainty, robustness, and global XAI, and tested its ability to calibrate trust. We then checked whether an algorithm that aims to provide more of an intuitive visual understanding, despite being complicated to understand, can provide higher user satisfaction and human interpretability.