Researchers are proposing a framework that would allow users to understand the rationale behind artificial intelligence (AI) decisions. The work is significant, given the push to move away from "black box" AI systems – particularly in sectors, such as military and law enforcement, where there is a need to justify decisions. "One thing that sets our framework apart is that we make these interpretability elements part of the AI training process," says Tianfu Wu, first author of the paper and an assistant professor of computer engineering at North Carolina State University. "For example, under our framework, when an AI program is learning how to identify objects in images, it is also learning to localize the target object within an image, and to parse what it is about that locality that meets the target object criteria. This information is then presented alongside the result." In a proof-of-concept experiment, researchers incorporated the framework into the widely-used R-CNN AI object identification system.

Researchers are proposing a framework for artificial intelligence (AI) that would allow users to understand the rationale behind AI decisions. The work is significant, given the push move away from "black box" AI systems--particularly in sectors, such as military and law enforcement, where there is a need to justify decisions. "One thing that sets our framework apart is that we make these interpretability elements part of the AI training process," says Tianfu Wu, first author of the paper and an assistant professor of computer engineering at North Carolina State University. "For example, under our framework, when an AI program is learning how to identify objects in images, it is also learning to localize the target object within an image, and to parse what it is about that locality that meets the target object criteria. This information is then presented alongside the result." In a proof-of-concept experiment, researchers incorporated the framework into the widely-used R-CNN AI object identification system.