tensorflow/tcav

TCAV is an interpretability technique for neural networks that measures how important high-level concepts (such as gender, color, or texture) are to a model’s predictions. It uses directional derivatives along concept vectors learned from example images to quantify concept importance per class. The method provides global explanations that apply to an entire prediction class rather than individual inputs, helping users understand model behavior without requiring machine learning expertise.