(Governing the Algorithm — Article #3)
A Question Everyone Assumes Someone Else Has Answered
Ask a vendor who's accountable if their AI produces a wrong output, and the honest answer is often some version of "it depends." Ask the hospital the same question, and they'll often point back to the vendor. Ask the clinician who acted on the AI's output, and they'll point to both.
This isn't because anyone is being evasive. It's because accountability for AI-assisted decisions runs through a chain that most companies have never explicitly mapped:
Vendor → Hospital → Clinician → Patient
Each link assumes responsibility sits somewhere else in the chain — until an actual incident forces the question, and everyone discovers the assumption was never resolved.
Why This Chain Breaks More Easily Than People Expect
✔ The vendor built and validated the model, but doesn't control how it's deployed, monitored, or acted on inside a specific hospital's workflow
✔ The hospital deployed the tool and is responsible for its use, but relies on the vendor's claims about performance and limitations
✔ The clinician made the final decision, but often did so trusting a tool the institution approved and the vendor validated
✔ The patient experiences the outcome, but has no visibility into any of the above
None of these parties is wrong to see it this way. The problem is that all four can hold their position simultaneously, and an incident review can stall for months while everyone works out who was actually supposed to be watching.
Where the Ambiguity Actually Gets Resolved (Or Doesn't)
"We assumed the hospital's clinical protocol would catch that."
"We assumed the vendor would have flagged that in their documentation."
Statements like these show up constantly in post-incident reviews — not because anyone acted in bad faith, but because accountability was never explicitly assigned before deployment. It was implied, informally distributed, and untested until the moment it mattered.
Governance that actually works closes this gap in advance, not during a crisis:
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A documented, contractually clear division of responsibility between vendor and hospital, specific to this product — not a generic liability clause
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A defined escalation path so a clinician who suspects an AI error knows exactly who to notify and what happens next
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Monitoring commitments that specify who is watching model performance in production, and what threshold triggers action
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Clear labeling and documentation of the AI's known limitations, so "the vendor didn't tell us" and "the hospital should have known" aren't both plausible after the fact
Why Ambiguity Is a Risk Even If Nothing Goes Wrong Yet
A hospital compliance or risk-management team doesn't need an actual incident to flag this gap — they'll ask about it during procurement. "Who's accountable if this is wrong?" is now a standard due-diligence question, and "that's not really been defined" is a disqualifying answer, regardless of how good the model is.
Vendors who can answer this clearly, with documentation to back it up, remove one of the most common reasons a promising pilot stalls in legal and risk review.
Final Thought
An AI system doesn't need to fail for accountability to matter. It needs to be unclear for trust to erode.
Undefined accountability isn't neutral.
It's a liability waiting for its first test case.
The companies that map this chain — vendor, hospital, clinician, patient — before deployment, not after an incident, are the ones who survive their first real error with their institutional relationships intact.
Next in the Governing the Algorithm Series:
Garbage In, Liability Out: Why Data Governance Is a Clinical Safety Issue
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