A-Z Index
Browse concept cards whose titles begin with M. This is useful when you want an alphabetical view of the library rather than browsing by governance topic or category.
Maintaining Compliance While Adapting Governance refers to the ongoing process of ensuring that AI systems adhere to legal, ethical, and organizational standards while also evolvin...
Maintaining consistency across governance decisions in AI refers to the alignment of policies, regulations, and practices across various levels of AI governance frameworks. This co...
Maintaining Governance Integrity Over Time refers to the continuous adherence to established AI governance frameworks and principles throughout the lifecycle of AI systems. This co...
Maintaining Internal Consistency Across Governance Decisions refers to the alignment and coherence of policies, regulations, and practices within an AI governance framework. This c...
Maintaining traceability when extending frameworks in AI governance refers to the ability to track and document changes made to governance frameworks as they evolve. This is crucia...
Mapping Risks to Framework Components involves identifying and categorizing potential risks associated with AI systems and aligning them with specific components of an AI governanc...
Mapping Use Cases to the AI Lifecycle involves aligning specific AI applications with the stages of the AI lifecycle, including data collection, model training, deployment, and mon...
Measuring the effectiveness of compliance programs involves assessing how well an organization adheres to established AI governance frameworks and regulations. This is crucial in A...
Maintaining coherent governance across jurisdictions refers to the alignment of AI regulations and policies among different legal frameworks and regions. This is crucial in AI gove...
Maintaining Governance Coherence Across Regions refers to the alignment and harmonization of AI governance frameworks and regulations across different jurisdictions. This is crucia...
Managing Data and Model Flows Across Regions involves the governance of data and AI model transfers between different jurisdictions, ensuring compliance with local laws and regulat...
Mapping Regulatory Obligations to Framework Controls involves aligning specific legal requirements from AI regulations, such as the EU AI Act, with internal governance frameworks a...
Minimal-risk AI systems refer to AI technologies that pose a low level of risk to rights and safety, such as chatbots or spam filters. In AI governance, identifying and categorizin...
Maintaining Risk Consistency Across Decisions refers to the practice of ensuring that risk assessments and management strategies are uniformly applied across all AI-related decisio...
Managing Risk Dependencies Across Domains involves identifying and addressing interdependencies between various risk factors that can affect AI systems across different sectors or...
Model Risk Beyond Bias refers to the potential for AI models to produce harmful outcomes not just due to biased data but also from inherent model design flaws, misalignment with ob...
Maintaining Governance Integrity During Crisis and Change refers to the processes and frameworks that ensure AI governance remains robust and effective during periods of disruption...
Making governance decisions with incomplete information refers to the process of formulating policies or regulations for AI systems when all relevant data or insights are not avail...
Making Trade-Offs with No Acceptable Option refers to the decision-making process in AI governance where stakeholders must choose between multiple undesirable outcomes due to inher...
Managing Governance Debt refers to the accumulation of unresolved governance issues, risks, and compliance gaps in AI systems over time. It is crucial in AI governance as it highli...
Managing trade-offs across multiple risks in AI governance involves balancing various potential harms and benefits associated with AI systems. This concept is crucial as it enables...
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