Towards Usable and Useful Explainable AI

Theoretical Foundations of Usable XAI

Several interpretation techniques have been developed to enhance the explainability of deep learning models. However, few of them have delved into a fundamental question: do we truly trust these explainers? In other words, are these interpretable methods faithful to the underlying models? This problem hinders the practical application of XAI. Thus, a usable XAI should provide faithful interpretations. I mainly focused on achieving Usable XAI from three aspects. Specifically, my research mainly focuses on improving the explanation faithfulness for current methods from three aspects: stability, controllability, and consistency.

Towards Useful XAI in Large Models

I also applied Useful XAI as a toolkit, which can serve as a guide for boosting utility and enhancing the trustworthiness of deep learning models. I mainly focus on the following three aspects via interpretability. First, Enhancing Trustworthiness. Useful XAI can serve as a tool to enhance trustworthiness, which is essential for LLMs. I mainly focus on using interpretability to mitigate safety challenges in LLM, such as hallucination, jailbreaking, privacy, and copyright infringement. Second, Boosting Utility. Useful XAI can also be a powerful tool to boost model performance. For example, XAI can be used as a modality for alignment with LLMs and Graph, reducing time and costs by 100X. Additionally, XAI can be used to fine-tune LLMs, equipping them with capabilities for self-aware error identification and correction. Third, Analyzing Models. Useful XAI can also serve as an analytical tool to reveal the deeper behaviors of LLMs.

Towards Usable XAI for Science

Usable XAI models are essential in AI applications for the science field because they enable researchers and practitioners to understand and trust the decisions made by complex models. Through a wide range of interdisciplinary collaborations, I have deployed these technologies for impactful applications in healthcare, traffic, material science, and marine science.