DARPA Program xAI ( Explainable Artificial Intelligence)

The Explainable AI (XAI) program aims to create a suite of machine learning techniques that:

Produce more explainable models, while maintaining a high level of learning performance (prediction accuracy).

Enable human users to understand, appropriately trust, and effectively manage the emerging generation of artificially intelligent partners.

New machine-learning systems will have the ability to explain their rationale, characterize their strengths and weaknesses, and convey an understanding of how they will behave in the future.

XAI is one of a handful of current DARPA programs expected to enable “third-wave AI systems”, where machines understand the context and environment in which they operate, and over time build underlying explanatory models that allow them to characterize real world phenomena.

The XAI program is focused on the development of multiple systems by addressing challenge problems in two areas: (1) machine learning problems to classify events of interest in heterogeneous, multimedia data; and (2) machine learning problems to construct decision policies for an autonomous system to perform a variety of simulated missions.

At the end of the program, the final delivery will be a toolkit library consisting of machine learning and human-computer interface software modules that could be used to develop future explainable AI systems. After the program is complete, these toolkits would be available for further refinement and transition into defense or commercial applications.