AI Governance
Good AI governance starts with the workflow and the people accountable for it. Explore how permitted information, access, review, exceptions, records and monitoring can be considered as part of an implementation. Requirements depend on the use case and applicable obligations.
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What does governance mean in practice?
The practical governance questions around permitted information, access, review, exceptions and accountability.
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When should AI governance be considered in an AI project?
How governance questions can inform project scoping, testing, implementation and handover from the outset.
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What should an AI governance framework cover?
The core areas an organisation may consider, including accountability, risk, data, oversight, monitoring and training.
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How does Muuvment address AI governance during implementation?
How governance needs can be discussed throughout qualification, testing, implementation and handover.
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How should people review AI outputs?
How to define reviewer roles, check supporting evidence and decide when AI-generated work should be escalated.
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How can an organization assess AI risks?
Questions about affected people, data, error consequences, applicable requirements and controls for a specific AI use case.
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What do explainability and auditability mean for AI governance?
How teams can think about understanding AI-supported results and keeping appropriate decision records.
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Does Muuvment certify AI systems or guarantee compliance?
What Muuvment implementation support covers and why it is not a certification or legal compliance guarantee.
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What should we prepare for an AI governance discussion?
The workflow, users, information, jurisdictions and existing controls to bring to an initial governance conversation.
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How can AI governance support privacy and data protection?
How data purpose, access, minimisation and service-specific privacy arrangements fit into governance discussions.
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