To Trust or Not to Trust: Towards a novel approach to measure trust for XAI systems

arXiv:2405.05766v1 Announce Type: cross
Abstract: The increasing reliance on Deep Learning models, combined with their inherent lack of transparency, has spurred the development of a novel field of study known as eXplainable AI (XAI) methods. These methods seek to enhance the trust of end-users in automated systems by providing insights into the rationale behind their decisions. This paper presents a novel approach for measuring user trust in XAI systems, allowing their refinement. Our proposed metric combines both performance metrics and …

abstract arxiv cs.ai cs.cv cs.lg deep learning development explainable ai novel reliance seek study systems transparency trust type xai