Explainer: The Black Box Problem

We see what goes into AI models, and we see what comes out. But what happens in between can be a mystery — one known as the black box problem.

AI can crunch more data and sift more text than any human ever could, finding complex connections and correlations. But unlike human problem-solvers, whose reasoning can be articulated and examined, AI systems operate through layers of computation that may be difficult to interpret.

The more AI is used to inform decisions, the more important it becomes to understand where its answers come from — particularly when they’re guiding outcomes in areas such as housing, hiring, and healthcare. Researchers at Stanford Graduate School of Business are developing tools to explain AI’s decision-making, working to illuminate the black box and how it affects people.

Watch this short explainer video to learn more.

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