Collaborating Authors

The How of Explainable AI: Explainable Modelling


Achieving explainable modelling is sometimes considered synonymous with restricting the choice of AI model to specific family of models that are considered inherently explainable. We will review this family of AI models. However, our discussion goes far beyond the conventional explainable model families and includes more recent and novel approaches such as joint prediction and explanation, hybrid models, and more. Ideally we can avoid the black-box problem from the beginning by developing a model that is explainable by design. The traditional approach to achieve explainable modelling is to adopt from a specific family of models that are considered explainable.

Hardware Acceleration of Explainable Machine Learning using Tensor Processing Units


Machine learning (ML) is successful in achieving human-level performance in various fields. However, it lacks the ability to explain an outcome due to its black-box nature. While existing explainable ML is promising, almost all of these methods focus on formatting interpretability as an optimization problem. Such a mapping leads to numerous iterations of time-consuming complex computations, which limits their applicability in real-time applications. In this paper, we propose a novel framework for accelerating explainable ML using Tensor Processing Units (TPUs).

Interpretable vs Explainable Machine Learning


From medical diagnoses to credit underwriting, machine learning models are being used to make increasingly important decisions. To trust the systems powered by these models we need to know how they make predictions. This is why the difference between an interpretable and explainable model is important. The way we understand our models and degree to which we can truly understand then depends on whether they are interpretable or explainable. Put briefly, an interpretable model can be understood by a human without any other aids/techniques.

Explainable Artificial Intelligence


Dramatic success in machine learning has led to a torrent of Artificial Intelligence (AI) applications. Continued advances promise to produce autonomous systems that will perceive, learn, decide, and act on their own. However, the effectiveness of these systems is limited by the machine's current inability to explain their decisions and actions to human users. The Department of Defense is facing challenges that demand more intelligent, autonomous, and symbiotic systems. Explainable AI--especially explainable machine learning--will be essential if future warfighters are to understand, appropriately trust, and effectively manage an emerging generation of artificially intelligent machine partners.