Human Interpretability

Human interpretability in artificial intelligence focuses on making AI models' decision-making processes understandable to humans, thereby increasing trust and facilitating effective use in sensitive applications. Current research emphasizes developing inherently interpretable models, such as those incorporating biological knowledge or leveraging simpler architectures like linear classifiers and kernel methods, alongside post-hoc explanation techniques for existing "black box" models. This work is crucial for advancing AI's adoption in fields like medicine and healthcare, where understanding the reasoning behind predictions is paramount for responsible and effective implementation.