Institutions introducing AI have an obligation to deploy it responsibly and explain its workings clearly to faculty, staff and students. Simply put, institutional decision-making around AI should center on “explainability”—that is, ensuring that the way a given AI tool works is transparent and understandable to humans.

When we talk about AI, it’s immediately apparent that there’s a tension between the quality of its output and its explainability. Most AI tools draw on a series of algorithms that instruct machines to analyze data, perform tasks and improve over time in ways that resemble human intelligence. It’s relatively simple to describe an automated process that makes music and movie recommendations based on an individual’s previously expressed preferences, but it’s nearly impossible to explain the inner workings of an algorithm that churns out essays after scanning billions of pages of written work. The decision-making processes of these deep-learning systems are locked away in an impenetrable black box. Even the builders of these large language models lack the keys that allow them entry.

Instant Insights /

Opening the black box: Helping faculty and staff understand an AI-driven world

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