AI in general 1, 2017

Explainable Artificial Intelligence (XAI)

USA

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DARPA

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Explainability

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[font=Arial, Verdana, sans-serif]DARPA is funding several programs aimed at developing XAI - a new machine learning process that is more understandable to humans. The effort is motivated by national security objectives. The aim is to “Produce more explainable models, while maintaining a high level of learning performance (prediction accuracy); and enable human users to understand, appropriately trust, and effectively manage the emerging generation of artificially intelligent partners.” Working on “a portfolio of methods that will provide future developers with a range of design options covering the performance-versus-explainability trade space.” “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. These two challenge problem areas were chosen to represent the intersection of two important machine learning approaches (classification and reinforcement learning) and two important operational problem areas for the DoD (intelligence analysis and autonomous systems).” 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.

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This database is an information resource about global governance activities related to artificial intelligence. It is designed as a tool to help researchers, innovators, and policymakers better understand how AI governance is developing around the world.

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