Concept Activation Vector

Concept Activation Vectors (CAVs) are a technique used to interpret the internal representations of complex machine learning models, particularly deep neural networks, by mapping human-understandable concepts onto the model's latent space. Current research focuses on improving CAV accuracy and robustness, addressing issues like inconsistent concept representation across layers and the limitations of linear assumptions, often employing techniques like energy-based models and knowledge graphs to enhance concept definition and retrieval. This work is significant for enhancing the explainability and trustworthiness of AI systems across diverse applications, from medical image analysis and natural language processing to robotics and autonomous vehicles, by bridging the gap between complex model outputs and human understanding.