The U.S. Defense Advanced Research Projects Agency (DARPA) has launched a program that will create the technology to make new generations of artificial intelligence (AI) systems "explainable." 

DARPA'S Explainable AI (XAI) program aims to create new machine learning methods to produce more explainable models and combine them with explanation techniques. And why the need to understand AI?

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That's because explainable AI -- especially explainable machine learning -- will be essential if future American warfighters are to understand, appropriately trust and effectively manage an emerging generation of AI "partners" such as battlefield robots and machines.

XAI is vital because continued advances in AI promise to produce autonomous systems that will perceive, learn, decide and act on their own. The effectiveness of these AI systems, however, is limited by the machine's current inability to explain their decisions and actions to human users.

The XAI program aims to create a suite of machine learning techniques that will produce more explainable models, while maintaining a high level of learning performance (prediction accuracy). It will also enable human users to understand, trust and manage the emerging generation of AI partners.

XAI will give rise to new machine-learning systems with the ability to explain their rationale, characterize their strengths and weaknesses and convey an understanding of how they will behave in the future.

The strategy for achieving that goal is to develop new or modified machine-learning techniques that will produce more explainable models. These models will be combined with state-of-the-art human-computer interface techniques capable of translating models into understandable and useful explanation dialogues for the end user.

DARPA's strategy is to pursue a variety of techniques to generate a portfolio of methods that will provide future developers with a range of design options covering the "performance-versus-explainability" trade space.

XAI will focus the development of multiple systems on addressing challenges problems in two areas: machine learning problems to classify events of interest in heterogeneous, multimedia data and 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 can be used to develop future explainable AI systems. These toolkits will be available for further refinement and transition into defense or commercial applications.

TagsDefense Advanced Research Projects Agenc, DARPA, Explainable AI (XAI) program, explainable machine learning, Artificial Intelligence, AI