Beginner
What is AI governance?
AI Governance is the framework of policies, processes, roles, controls, and oversight used to steer AI development and use across its lifecycle. It aims to ensure responsible, lawful, secure, transparent, and accountable AI practice within an organization or jurisdiction.
What is an AI management system?
An AI Management System is an organizational platform or structured set of processes for managing AI projects and systems throughout their lifecycle. It coordinates governance, data flows, model development, deployment, monitoring, compliance, and accountability activities.
What is ISO/IEC 42001?
ISO/IEC 42001 is an international standard for establishing, implementing, maintaining, and improving an AI management system. It helps organizations govern AI development and use through policies, roles, risk management, oversight, and continual improvement.
What is an AI inventory system register?
An AI Inventory or System Register is an organized catalogue of AI systems and use cases within an organization. It typically records owners, purposes, data sources, deployment status, risks, controls, and other information needed for governance, audit, and accountability.
Intermediate
How is AI risk assessment different from AI audit?
Use AI Risk Assessment to identify and manage risks; use AI Audit to evaluate evidence, controls, or claims against defined criteria.
What is AI assurance?
AI Assurance is the set of practices that provide justified confidence that an AI system works as intended in its operational context.
What is shadow AI?
Shadow AI is the use of AI tools or systems without formal approval, oversight, or governance by an organization. It creates risks around data leakage, compliance, security, and inconsistent decision-making, and is typically managed through policy, approved alternatives, training, and monitoring.
What is an AI use policy?
An AI Use Policy is an internal governance document that defines how AI tools and systems may be used within an organization. It typically covers acceptable and prohibited uses, roles and responsibilities, data handling, approval requirements, monitoring, and incident escalation.
What is vendor due diligence for AI?
AI vendor due diligence is the assessment of third-party AI products or providers for security, privacy, reliability, governance, and compliance risks. It helps organizations decide whether and how a vendor can be safely approved, procured, monitored, or restricted.
What is algorithmic accountability?
Algorithmic Accountability is responsibility for the design, operation, decisions, and impacts of algorithmic systems. It involves transparency, traceability, oversight, and mechanisms for explaining, contesting, or remedying harmful outcomes.
Advanced
What is an AI model card?
An AI Model Card is a concise documentation artifact that accompanies a trained model or AI service. It typically describes intended use, limitations, evaluation results, relevant risks, data or training context, and other information that supports transparency and responsible deployment.
What is transparency in AI governance?
Transparency is the degree to which an AI system’s purpose, data use, operation, limitations, and outputs can be understood or examined by relevant stakeholders.