AI-specific governance
AI governance is the set of policies, processes, controls, and oversight mechanisms for directing and managing the development and use of artificial-intelligence systems.
Introduction to Generative AI, Second Edition
AI governance is the framework of policies, institutions, processes, and oversight mechanisms that direct and constrain the development and use of artificial-intelligence systems.
- How it is used
- Frames ways to manage generative AI’s social and technical risks as development accelerates.
- How deep it goes
- Explains governance goals and contrasts voluntary commitments with regulation, without giving an implementation procedure.
- Examples
- Compares the EU AI Act, US AI Action Plan, Chinese interim measures, and GDPR’s role in data governance.
- What you can do after
- Evaluate/understand it by distinguishing company commitments from public rules and asking how each supports accountability.
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The Art of AI Product Development
AI governance is the set of policies, controls, and practices for identifying and managing risks arising from AI systems.
- How it is used
- Guides AI teams in managing risk while building trustworthy, compliant systems across the development lifecycle.
- How deep it goes
- Maps governance dimensions, controls, and a five-phase shift-left process, stopping short of detailed implementation procedures.
- Examples
- Uses AI recommendations across regulated industries, governance incidents, security threats, bias testing, audit trails, and a lifecycle roadmap.
- What you can do after
- Apply governance by design across ideation, development, and deployment using a proactive lifecycle roadmap.
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First-Party Data Activation
AI governance is the policies, processes, and controls for directing and overseeing the development and use of artificial-intelligence systems.
- How it is used
- Directs generative marketing adoption amid legal, privacy, ethical, cost, and organizational risks.
- How deep it goes
- Maps major risks and initial compliance checks, but stops short of specifying a complete governance framework.
- Examples
- Uses third-party AI providers, marketing campaigns, customer data, autonomous decisions, employee training, and personalized communications as governance contexts.
- What you can do after
- Operate/configure generative marketing adoption by reviewing providers, data handling, intellectual property, human oversight, and workforce readiness.
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Related concepts
- traceabilityuses · Traceability is the ability to follow an artifact, data, result, or decision through its origins, relationships, transformations, and relevant history.
- shift leftused by · Shift left is the practice of performing testing, security, operations, compliance, or other quality activities earlier in the software development lifecycle.
- fairnesshas part · Fairness is the principle or property of allocating treatment, opportunities, or outcomes equitably among relevant people, groups, or participants according to appropriate criteria.
- transparencyhas part · Transparency is the degree to which underlying content is visible through an element or relevant information, processes, and decisions are accessible and understandable to observers.
- role-based access controluses · Role-based access control is an authorization model that assigns permissions to roles and grants users or other principals access through their assigned roles.
- context assemblyconfigures · Context assembly is the process of selecting, organizing, and combining relevant information and instructions into context for a model or other downstream processing.
These guides are generated from the text of Manning books by Manning's knowledge graph and link to the sections they draw on. The full books are available on liveBook and at manning.com.