Google's AI explains how image classifiers made their decisions

It's often assumed that as the complexity of an AI system increases, it becomes invariably less interpretable. But researchers have begun to challenge that notion with libraries like Facebook's Captum, which explains decisions made by neural networks with the deep learning framework PyTorch, as well as IBM's AI Explainability 360 toolkit and Microsoft's InterpretML. In a bid to render AI's decision-making even more transparent, a team hailing from Google and Stanford recently explored a machine learning model -- Automated Concept-based Explanation (ACE) -- that automatically extracts the "human-meaningful" visual concepts informing a model's predictions. As the researchers explain in a paper detailing their work, most machine learning explanation methods alter individual features (e.g., pixels, super-pixels, word-vectors) to approximate the importance of each to the target model. This is an imperfect approach, because it's vulnerable to even the smallest shifts in the input.