A-Z Index
Browse concept cards whose titles begin with A. This is useful when you want an alphabetical view of the library rather than browsing by governance topic or category.
AI Governance Implications of Risk Classification refers to the systematic categorization of AI systems based on their potential risks and impacts on society. This classification i...
AI Governance refers to the frameworks, policies, and processes that guide the development and deployment of artificial intelligence technologies, ensuring they align with ethical...
AI Lifecycle Stages refer to the systematic phases an AI system undergoes from design to decommissioning. These stages typically include planning, development, deployment, monitori...
AI Policy, AI Standard, and AI Procedure are three distinct yet interconnected components of AI governance. An AI Policy outlines the overarching principles and objectives guiding...
In AI governance, the distinction between an AI System Owner and an AI User is crucial. The AI System Owner is responsible for the development, deployment, and overall management o...
An AI System refers to the complete setup that includes hardware, software, and data to perform tasks using artificial intelligence. An AI Model is a mathematical representation or...
Accountability as a governance principle in AI refers to the obligation of organizations and individuals to take responsibility for the outcomes of AI systems. This principle is cr...
Accountability for High-Risk AI Systems refers to the responsibility of organizations and individuals to ensure that AI systems classified as high-risk are designed, implemented, a...
In the context of AI governance, accountability refers to the obligation of individuals or organizations to answer for the outcomes of AI systems, while responsibility pertains to...
Accountability, responsibility, and authority are critical components of AI governance that delineate roles in decision-making processes. Accountability refers to the obligation to...
Adapting Compliance Strategy to Emerging Rules involves the proactive adjustment of an organization's compliance framework to align with new regulations and standards in AI governa...
Aligning AI Governance Roadmaps with Enterprise Roadmaps involves integrating AI governance strategies with the broader organizational objectives and strategic plans of an enterpri...
Aligning Compliance with Business Strategy refers to the process of ensuring that an organization's AI governance frameworks and compliance measures are integrated with its overall...
Aligning Ethics, Risk, Law, and Strategy Coherently refers to the integration of ethical considerations, legal frameworks, risk management, and strategic objectives in AI governanc...
Aligning Framework Design with Operating Models refers to the process of ensuring that the governance frameworks established for AI systems are compatible with the operational stru...
Aligning governance decisions across time horizons refers to the strategic approach of ensuring that AI governance frameworks consider both immediate and long-term impacts of AI te...
Aligning governance decisions with organizational purpose involves ensuring that AI governance frameworks, policies, and practices reflect the core mission and values of an organiz...
Aligning Governance Models with Compliance Frameworks refers to the integration of organizational governance structures with regulatory compliance requirements specific to AI techn...
Aligning Governance Models with Strategic Compliance Goals involves integrating an organization's governance framework with its compliance objectives, particularly in the context o...
Aligning Long-Term Governance Strategy with Day-to-Day Decisions refers to the process of ensuring that the everyday operational choices made within an AI organization are consiste...
Articulating a coherent AI governance philosophy involves establishing a clear framework of principles, values, and objectives that guide the development, deployment, and regulatio...
Artificial Intelligence (AI) refers to systems that can perform tasks typically requiring human intelligence, such as learning, reasoning, and problem-solving. In contrast, traditi...
Assessing Governance Defensibility Under Scrutiny refers to the process of evaluating the robustness and transparency of AI governance frameworks when subjected to external examina...
Assurance activities within compliance frameworks refer to systematic processes designed to evaluate and verify that AI systems adhere to established regulations, standards, and et...
The assurance implications of different governance models refer to how various frameworks for AI governance influence the accountability and reliability of AI systems. These models...
Assurance Readiness for High-Risk AI refers to the preparedness of AI systems to undergo rigorous evaluation and validation processes to ensure they meet established safety, ethica...
Assurance, compliance, and audit are three critical components in AI governance that ensure algorithmic accountability. Assurance refers to the confidence that AI systems operate a...
Autonomy and decision-making in AI systems refer to the capability of AI to make choices and take actions without human intervention. This concept is crucial in AI governance as it...
AI Act Expectations for Risk Documentation refer to the regulatory requirements set forth in the EU AI Act that mandate organizations to systematically document the risks associate...
AI Act Expectations for Sandbox Participation refer to the regulatory framework established under the EU AI Act that allows companies to test AI systems in a controlled environment...
AI Act Risk Categories classify AI systems based on their potential risks to rights and safety. The categories are 'Unacceptable,' 'High,' 'Limited,' and 'Minimal' risk. This class...
The Accountability Principle under the General Data Protection Regulation (GDPR) mandates that organizations must not only comply with data protection laws but also demonstrate the...
Accuracy and Data Quality refer to the correctness, reliability, and relevance of data used in AI systems. In AI governance, ensuring high data quality is crucial as it directly im...
Annex III High-Risk Use Case Categories refer to specific applications of AI systems identified as posing significant risks to rights and safety, as outlined in regulatory framewor...
Anticipating AI Act Interpretation Through Precedent involves analyzing previous legal cases and regulatory decisions to predict how current and future AI regulations, such as the...
Anticipating Framework Alignment with Future Regulation refers to the proactive approach organizations take to ensure their AI systems comply with anticipated regulatory changes. T...
Applicable Law in Cross-Border AI Systems refers to the legal frameworks that govern the use and deployment of AI technologies across different jurisdictions. This concept is cruci...
Applying AI Act Categories to AI Use Cases involves classifying AI systems based on their risk levels as outlined in regulatory frameworks, such as the EU AI Act. This categorizati...
Automated Decision-Making in Courts and Regulators refers to the use of AI systems to assist or make decisions in legal and regulatory contexts. This concept is crucial in AI gover...
AI Risk Appetite and Tolerance Statements are formal declarations by an organization that outline the level of risk it is willing to accept in the deployment and use of AI technolo...
AI Risk refers to the unique challenges and uncertainties associated with artificial intelligence systems, which differ significantly from traditional IT risks. While traditional I...
Adapting Risk Controls to Novel Threats refers to the proactive adjustment of risk management frameworks in response to emerging and unforeseen risks associated with AI technologie...
Assessing Materiality of Bias Risks involves evaluating the significance of potential biases in AI systems and their impact on decision-making processes. This concept is crucial in...
Assumptions and constraints in AI use cases refer to the predefined beliefs and limitations that guide the development and deployment of AI systems. These elements are crucial in A...
Automated Decision-Making (ADM) refers to the use of algorithms and AI systems to make decisions without human intervention. In the context of AI governance, it is crucial to ensur...
Acceptable Risk vs Unacceptable Harm refers to the balance between the potential benefits of AI technologies and the risks they pose to individuals and society. In AI governance, t...
Adapting Frameworks Under Stress and Change refers to the ability of AI governance frameworks to evolve in response to unforeseen challenges, technological advancements, or shifts...
Adapting Governance to Organisational Resistance involves modifying AI governance frameworks to address and mitigate internal resistance within organizations. This resistance can s...
Analysing Governance Performance During Investigations involves evaluating the effectiveness and efficiency of AI governance frameworks when addressing compliance issues or breache...
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