AI governance · · WebLab Corp editorial
Connect AI product design with ownership, evaluation, monitoring, and human review using concepts from IBM's AI governance guidance.
Governance is more than a model setting
IBM describes AI governance through processes, standards, safeguards, and oversight. It spans how AI is developed and used, including risks around bias, privacy, and misuse. Governance is an operating responsibility, not a guarantee attached to a particular model.
Translate principles into product behavior
WebLab Corp's implementation perspective is to specify which actions need approval, what users should see about an answer's source, and who reviews failures. Define the fallback when the model is uncertain or the required information is unavailable.
Keep ownership visible after launch
Assign responsibility for evaluation cases, monitoring, and changes to data access. Review the workflow as the use case evolves. Legal and regulatory obligations need qualified advice for the relevant jurisdiction; this article is technical orientation, not a compliance determination.
Sources and further reading
Original WebLab Corp editorial, informed by the linked IBM resources. IBM does not sponsor or endorse this article. Implementation recommendations are WebLab Corp's perspective.
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