Phase 1 of this series covered the governance reality every AI vendor eventually faces: clearance isn't governance, a model isn't a management system, and accountability has to be assigned before an error happens, not after.
Phase 2 starts underneath all of that, at the layer most companies think of as a technical concern rather than a clinical one:
The data feeding your AI is not a back-office detail. It's a patient safety input.
(Governing the Algorithm, Article #4)
Where "Garbage In, Garbage Out" Stops Being an Engineering Cliché
In most software contexts, bad input data produces a bad report, a broken dashboard, an annoying bug. In Healthcare AI, bad input data produces a bad clinical output: a missed finding, a false confidence score, a recommendation built on a population the model was never actually validated against.
The engineering cliché "garbage in, garbage out" understates what's actually at stake here. In this context, it's more accurate to say:
Garbage in, liability out.
Data Governance Is Not a Back-Office Function
Founders often organize their company as if data governance belongs to engineering, and clinical safety belongs to the regulatory or clinical team. In an AI-based device, that division doesn't hold, the data pipeline is a direct input into a clinical decision. A real data governance function has to answer:
✔ Provenance: where did this data actually come from, and can that be verified?
✔ Representativeness: does the training and monitoring data reflect the populations the device will actually be used on?
✔ Currency: is the model still being fed data that reflects current clinical practice, or is it quietly drifting from reality?
✔ Integrity: is there a documented process preventing corrupted, mislabeled, or duplicated data from entering the pipeline unnoticed?
None of these are questions a validation study answers once and then closes. They're ongoing operational questions that determine whether the model's original performance claims still hold in production.
Why This Gets Deprioritized
Data governance rarely fails because a company doesn't care about it. It fails because it's invisible when it's working and only becomes visible when it's already caused a problem. A validation study is a fixed, demonstrable deliverable. A living data governance process is ongoing, unglamorous, and easy to under-resource, right up until a subgroup performance issue or a mislabeled data source surfaces in production.
By then, it's not a data engineering problem anymore. It's a patient safety incident with a data engineering root cause.
What This Looks Like in Practice
Companies that treat data governance as a clinical safety function, not an IT function, tend to build a few concrete things early:
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A documented data provenance record for every training and monitoring dataset, not just a summary in the validation report
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Defined population representativeness checks before deployment, so gaps are known rather than discovered
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A monitored pipeline that flags anomalies (missing fields, label drift, unexpected value ranges) before they silently degrade model behavior
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Clear ownership: a named function (not "the data team" in the abstract) responsible for data quality as an ongoing commitment, not a one-time deliverable
Final Thought
An algorithm is only as trustworthy as the data feeding it: not just at validation, but every day it remains in clinical use.
Bad data doesn't stay bad data.
It becomes a bad outcome.
Treating data governance as a compliance checkbox instead of a clinical safety function is one of the quieter ways a well-validated AI product ends up at the center of an incident nobody saw coming.
Next in the Governing the Algorithm Series:
The Bias Nobody Tested For: Why Demographic Performance Gaps Are a Governance Failure
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