In the last seminar of our XAI series, Iván Rodríguez from appliedAI talks about Concept Activation Vectors (CAVs). CAVs go beyond feature attribution and bring a quantitative approach to testing. He will discuss how this tool interprets a neural network’s internal state in terms of human-friendly concepts.
Abstract: In this XAI seminar, we’ll start by diving into the testing with Concept Activation Vectors (TCAV) method, which helps us gauge how much a model’s prediction is influenced by a user-defined concept. Additionally, we’ll explore two other papers, Concept Bottleneck Models (CBM) and Post-Hoc CBM, which present clever approaches to engage with user-defined concepts. For instance, if the model initially overlooked a bone spur on an X-ray, would it still predict severe arthritis?