Implementing Transparent AI

Most institutions think they’ve addressed transparency once they’ve published a statement or added a sentence to their data policy. The problem is, students and staff don’t experience AI through policies. They experience it through decisions. And if those decisions feel random, confusing, or unexplainable, trust breaks—no matter how ethical the system is on paper.

That’s why real transparency isn’t what you say about the technology. It’s what the people using it understand.

A counselor sitting down to start their day doesn’t need a PDF that outlines the ethics of your prioritization model. They need to know why a student showed up on today’s list. If they can’t answer that, they’re left guessing. Some will rely on instinct and override the system. Others will stop using it altogether. Either way, the result is the same: the trust you thought you built isn’t there when it counts.

This is where transparency moves from theory to practice.

When we talk about explainability in AI, it’s easy to slip into legal or technical language. But for admissions teams, it’s much simpler. Can your counselors explain how a decision was made? Can a leader describe why one student is ranked above another? Can your team tell a student why they’re getting certain messages or invitations?

If the answer is no, the system may still be accurate—but it isn’t clear.

And without clarity, trust doesn’t hold.

That’s why transparency has to be built into the daily workflow. When your team sees a score, they should also see the behavior behind it. When they follow a list, they should know why those students matter now. When they act, they should be acting from understanding, not assumption.

This isn’t about making the algorithm public. It’s about making the logic visible.

Emily Smith said it well during our recent conversation: you don’t earn trust by saying your system is fair. You earn it when your team understands how the system behaves—and believes that it matches your mission.

If you missed that discussion, it’s worth watching. Emily doesn’t talk about AI as an abstract tool. She talks about how institutions build confidence through clear decisions, not big declarations.

In admissions, ethical AI isn’t a one-time statement. It’s a series of small, visible decisions that help your team stay grounded, even as the tools get more advanced.

Watch the full video podcast with Emily Smith here: crowdcast.io/c/vpemily