This workshop aims to examine the state of the field of explainable AI (XAI) for computer vision, with the following goals: (1) discussion and dissemination of ideas at the cutting-edge of XAI research, and (2) a critical introspection on the challenges faced by the community and the way forward. The workshop includes papers, talks on recent advances, and a formal debate among invited speakers on the field’s core issues. We hope to encourage brainstorming in the community to bridge the gap from theory to practice and address challenges brought forth by the rise of large-scale foundation models, such as fundamentally rethinking what one wants from an explanation, obtaining it, performing appropriate evaluations, complying with regulatory requirements, and maintaining model performance.