The people affected · governance from the other side of the decision
Protect the institution — and the person the AI decides about.
Every risk on this site ultimately lands on a person — a student who was flagged, an applicant screened out, a faculty member whose work was scored. Governance that only asks "what's our exposure?" tells half the story. The measure of a system is whether the person on the receiving end has any recourse.
Why this matters
Contestability is both an equity commitment and a risk control. A system people can question surfaces its own errors — the appeals are your early-warning signal for bias and failure, long before a lawsuit or a headline.
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Notice
People are told, in advance and in plain terms, when AI meaningfully shapes a decision about them — an admission, a grade, a flag, a service denial.
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Explanation
They can get a human-understandable account of what factors mattered and why — not a model dump, but a reason they can act on.
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Appeal
There is a real, reachable path to have a human review and override the outcome, with a defined owner and a response time.
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Redress
When an AI decision causes harm, there is a way to correct the record, remedy the effect, and feed the failure back into the system.
What it looks like in practice · a student flagged by an early-alert model
Without contestability
A retention model flags a student as "high risk." Advising quietly reroutes them to a remedial track. The student is never told, can't see why, and has no way to say the model misread a medical leave. The error compounds silently.
With contestability
The student is notified a model informed the outreach, gets a plain explanation of the top factors, can appeal to a named advisor, and their correction feeds back — redress — improving the model for everyone.