A-Z Index
Browse concept cards whose titles begin with I. This is useful when you want an alphabetical view of the library rather than browsing by governance topic or category.
Identifying Systemic Weaknesses in Governance Design refers to the process of analyzing and evaluating the frameworks and structures that govern AI systems to uncover vulnerabiliti...
Incorporating Emerging Risks into Existing Frameworks refers to the process of updating and adapting AI governance frameworks to account for new and unforeseen risks associated wit...
Independent Review and Challenge Functions refer to mechanisms within AI governance frameworks that allow for objective assessment and scrutiny of AI systems and their outcomes. Th...
Integrating AI Governance into Enterprise Risk Management (ERM) involves embedding AI-related risks into the broader risk management framework of an organization. This integration...
Integrating AI Governance with Data Governance involves aligning the frameworks, policies, and practices that govern AI systems with those that manage data quality, privacy, and se...
Integrating AI Governance with Enterprise Risk Management (ERM) involves aligning AI governance frameworks with an organization's overall risk management strategies. This integrati...
Integrating AI Governance with Procurement and Vendor Risk involves aligning AI governance frameworks with procurement processes to ensure that third-party vendors comply with ethi...
Integrating AI Governance with Security and Resilience involves aligning AI governance frameworks with security protocols and resilience strategies to ensure that AI systems are no...
Integrating Ethics, Law, Risk, and Strategy Seamlessly refers to the holistic approach in AI governance that aligns ethical considerations, legal compliance, risk management, and s...
Integrating Law, Ethics, Risk, and Strategy in AI governance refers to the holistic approach of aligning legal frameworks, ethical standards, risk management practices, and strateg...
Integrating New Governance Domains into Existing Structures refers to the process of incorporating emerging regulatory frameworks and ethical considerations into established AI gov...
Integrating Sandbox Learnings into Compliance Frameworks involves the systematic incorporation of insights and data gathered from AI regulatory sandboxes into existing compliance s...
Internal Escalation During Enforcement Events refers to the structured process within an organization for raising and addressing issues related to AI compliance and ethical breache...
Incorporating regulatory foresight into governance plans involves proactively identifying and integrating potential future regulations and policy trends into AI governance framewor...
Integrity and Confidentiality in AI governance refers to the principles ensuring that data is accurate, reliable, and protected from unauthorized access or alterations. This is cru...
Interpreting Draft Regulations and Soft Law refers to the process of analyzing proposed legal frameworks and non-binding guidelines related to AI technologies. This concept is cruc...
In-scope vs out-of-scope decisions refer to the classification of decisions made during AI project development based on their relevance to the project's defined objectives and ethi...
Incident Response Roles and Responsibilities refer to the defined duties and tasks assigned to individuals or teams in the event of an AI-related incident, such as a data breach or...
In AI governance, 'Incidents,' 'Issues,' and 'Defects' are distinct concepts crucial for effective incident and issue management. An 'Incident' refers to an unplanned event that di...
Internal transparency for decision-makers refers to the clarity and openness regarding AI systems' operations, data usage, and decision-making processes within an organization. Thi...
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