Self Explaining

Self-explaining models aim to create artificial intelligence systems that not only produce predictions but also offer readily understandable justifications for those predictions, addressing the "black box" problem of many machine learning models. Current research focuses on developing novel architectures, such as variational autoencoders and case-based reasoning networks, and employing techniques like contrastive learning and prototype-based explanations to improve the transparency and trustworthiness of these models. This work is significant because it enhances the reliability and usability of AI in various fields, particularly those with high stakes, such as medicine, by providing insights into model decision-making processes and facilitating human-AI collaboration.