A-Z Index
Browse concept cards whose titles begin with E. This is useful when you want an alphabetical view of the library rather than browsing by governance topic or category.
Embedding accountability into framework design refers to the integration of mechanisms that ensure responsibility for AI systems throughout their lifecycle. This includes defining...
Embedding governance in product and delivery teams involves integrating governance frameworks and compliance measures directly into the workflows of teams responsible for AI produc...
Embedding risk tolerance into compliance controls refers to the integration of an organization's risk appetite into its regulatory and compliance frameworks concerning AI systems....
Ensuring coherence across governance artefacts involves aligning policies, procedures, and frameworks that guide AI development and deployment. This coherence is crucial in AI gove...
Escalation Paths for High and Emerging Risks refer to predefined procedures and protocols within an organization for identifying, assessing, and addressing significant risks associ...
Escalation triggers in AI systems are predefined conditions or thresholds that prompt the system to escalate decision-making to a higher authority or human intervention. This conce...
Ethical Consistency Across Complex Decisions refers to the principle that AI systems should apply the same ethical standards uniformly across various contexts and decisions. This c...
Ethical Reasoning Reflected in Case Outcomes refers to the practice of ensuring that AI systems make decisions based on ethical principles that align with societal values. This con...
Ethical risk refers to the potential for harm or negative consequences arising from the moral implications of AI technologies, while legal risk pertains to the likelihood of violat...
Ethical vs Legal vs Commercial Considerations in AI governance refers to the balance and interplay between ethical principles, legal requirements, and commercial interests in the d...
Evaluating Governance Effectiveness vs Existence refers to the assessment of not just whether AI governance frameworks are in place, but how well they function in practice. This co...
Evidence-Based AI Governance refers to the practice of making decisions regarding AI systems based on empirical data and rigorous analysis. This approach is crucial for ensuring al...
Evidence of Fairness and Bias Controls refers to the systematic processes and methodologies used to assess, document, and ensure that AI algorithms operate without unfair biases ag...
Evolving Compliance Frameworks Over Time refer to the dynamic structures and guidelines that govern the ethical and legal use of AI technologies. These frameworks must adapt to tec...
Evolving Framework Components Over Time refers to the iterative process of updating and refining AI governance frameworks to adapt to technological advancements, regulatory changes...
Explaining ethical decisions to stakeholders involves clearly communicating the rationale behind AI systems' decisions, particularly those that impact individuals or communities. T...
Explaining fairness decisions to stakeholders involves clearly communicating the rationale behind AI systems' fairness-related choices, such as algorithmic bias mitigation or equit...
Ensuring defensibility across jurisdictions and domains refers to the ability of AI systems and their governance frameworks to comply with varying legal, ethical, and regulatory st...
Early Cross-Border Risk Indicators refer to metrics and signals that help identify potential risks associated with AI systems operating across different jurisdictions. In AI govern...
Early Risk Signals During Use Case Design refer to the proactive identification of potential risks associated with an AI application during its initial design phase. This concept i...
The Ethical Evaluation of Fairness Trade-Offs involves assessing the balance between competing fairness criteria in AI systems, such as equality of opportunity versus overall accur...
Evaluating Risk Management Effectiveness Across Portfolios involves assessing how well risk management strategies perform across different AI projects or initiatives within an orga...
Explainability Expectations for Data Subject Requests refer to the obligation of organizations to provide clear, understandable explanations to individuals (data subjects) about ho...
Eligibility and Scope of Sandbox Participation refers to the criteria and boundaries that define who can engage in regulatory sandboxes designed for AI experimentation. These sandb...
Escalation When No Clear Policy Exists refers to the process of elevating decisions or issues to higher management or governance bodies when existing policies do not provide guidan...
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