#AAAI2024 workshops round-up 4: eXplainable AI approaches for deep reinforcement learning, and responsible language models

Deep reinforcement learning (DRL) has recently made remarkable progress in several application domains, such as games, finance, autonomous driving, and recommendation systems. However, the black-box nature of deep neural networks and the complex interaction among various factors raise challenges in understanding and interpreting the models' decision-making processes. This workshop brought together researchers, practitioners, and experts from both the DRL and the explainable AI communities to focus on methods, techniques, and frameworks that enhance the explainability and interpretability of DRL algorithms. The responsible language models (ReLM) workshop focused on the development, implementation, and applications of LMs aligned with responsible AI principles. Both theoretical and practical challenges regarding the design and deployment of responsible LMs were discussed, including bias identification and quantification, bias mitigation, transparency, privacy and security issues, hallucination, uncertainty quantification, and various other risks associated with LMs.