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.
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.
We invite applications for a tenure-track position in computer science, focused on explainable artificial intelligence, and ability to collaborate with social sciences. DKE research lines include human-centered aspects of recommender systems, as well as a strong applied mathematics component such as dynamic game theory (differential, evolutionary, spatial and stochastic game theory). The position is supported by the large and growing Explainable and Reliable Artificial Intelligence (ERAI) group of DKE. The group consists of Associate & Assistant Professors, postdoctoral researchers, PhD candidates and master/bachelor students. The ERAI group works together closely on a day-to-day basis, to exchange knowledge, ideas, and research advancements.
Explainable AI cannot be implemented as an afterthought or add-on to an existing system. It must be part of the original design. Beyond Limits systems cover the full spectrum of explainability, providing high-level system alerts, plus drill-down reasoning traces with detailed evidence, probability, and risk. Explainable AI helps take the mystery out of the technology and is the first step in enabling artificial intelligence to work with people in a trusting and mutually beneficial relationship.