Explainability won’t save AI
Much of artificial intelligence, and particularly deep learning, is plagued by the “black box problem.” While we may know the inputs and outputs of a model, in many cases we do not know what happens in between. AI developers make choices about how to design the model and the learning environment, but they typically do not determine the value of specific parameters and how an answer is reached. The lack of understanding about how an AI system works, in some cases even by the people who have developed it, is one of the reasons AI poses novel safety, ethical, and legal considerations, and why oversight and governance are especially important. Black box deep learning models are vulnerable to adversarial attacks and prone to racial, gender, and other demographic biases. Opacity is especially problematic in high-stakes settings such as health care, lending, and criminal justice, where significant harms have already been reported.
Explainable AI (XAI) is often offered as the answer to the black box problem and is broadly defined as “machine learning techniques that make it possible for human users to understand, appropriately trust, and effectively manage AI.” Around the world, explainability has been referenced as a guiding principle for AI development, including in Europe’s General Data Protection Regulation. Explainable AI has also been a major research focus of the Defence Advanced Research Projects Agency (DARPA) since 2016. However, after years of research and application, the XAI field has generally struggled to realize the goals of understandable, trustworthy, and controllable AI in practice.
