A-Z Index
Browse concept cards whose titles begin with W. This is useful when you want an alphabetical view of the library rather than browsing by governance topic or category.
Algorithmic accountability refers to the obligation of organizations to ensure that their algorithms operate transparently, fairly, and responsibly. In AI governance, it is crucial...
Expert review of AI governance involves a systematic evaluation by qualified professionals to assess the ethical, legal, and operational aspects of AI systems. This process is cruc...
Expert-level AI governance refers to the advanced frameworks and practices that ensure the responsible development, deployment, and oversight of AI systems. It encompasses comprehe...
Integrated AI Governance refers to a cohesive framework that aligns AI strategies, policies, and practices across an organization to ensure ethical, transparent, and accountable AI...
An AI Compliance Framework is a structured set of guidelines, standards, and practices designed to ensure that AI systems operate within legal, ethical, and regulatory boundaries....
An AI Governance Model is a structured framework that outlines the policies, processes, and responsibilities for managing AI systems within an organization. It is crucial for ensur...
The 'When and Why Framework Extension' in AI governance refers to the systematic evaluation and adaptation of existing governance frameworks to address emerging challenges and comp...
The concept of 'Who Decides Ethical Boundaries in Organisations' refers to the processes and roles within an organization that determine the ethical standards and guidelines for AI...
The concept of 'Who Decides What Is Fair Enough' in AI governance refers to the processes and stakeholders involved in determining fairness criteria for AI systems. This is crucial...
The concept of 'Who Owns an AI Use Case' refers to the identification of stakeholders responsible for the development, deployment, and outcomes of specific AI applications. This is...
The ownership and approval of impact assessments in AI governance refer to the designated individuals or bodies responsible for evaluating the potential effects of AI systems on so...
AI governance cannot operate in isolation because it requires integration across multiple domains, including ethics, law, technology, and social impact. This interconnectedness is...
Strategic planning in AI governance involves the systematic approach to setting goals, determining actions to achieve those goals, and mobilizing resources to execute the actions e...
Ethics in AI governance refers to the principles and values that guide the development, deployment, and use of artificial intelligence systems. It is crucial because ethical framew...
Cross-Border AI refers to the deployment and use of artificial intelligence systems that operate across different national jurisdictions, involving the transfer of data and algorit...
A high-risk AI system is defined by its potential to significantly impact individuals' rights, safety, or well-being, particularly in sensitive areas such as healthcare, law enforc...
The concept of 'Where AI Decisions Are Made vs Where Data Is Stored' refers to the distinction between the physical location of data storage and the location where AI algorithms pr...
Case law refers to the body of judicial decisions that interpret and apply laws, serving as precedents for future cases. In AI governance, case law is crucial as it shapes legal st...
Cross-border context increases governance risk in AI due to varying legal frameworks, data protection regulations, and ethical standards across jurisdictions. This disparity can le...
Emerging regulation in AI governance refers to new legal frameworks and policies being developed to address the unique challenges posed by artificial intelligence technologies. Thi...
Bias in AI systems refers to the systematic favoritism or discrimination that occurs when algorithms produce results that are prejudiced due to flawed training data, model design,...
An AI use case refers to a specific application of artificial intelligence technology to solve a defined problem or achieve a particular goal within an organization. In the context...
The concept of 'When Risk Becomes Unacceptable' in AI governance refers to the threshold at which the potential harms or negative consequences of an AI system outweigh its benefits...
The concept of when a use case should be stopped or redesigned refers to the critical evaluation of AI applications to determine if they pose unacceptable risks or ethical concerns...
An AI Impact Assessment (AIIA) is a systematic evaluation process that determines the potential effects of an AI system on individuals, society, and the environment before its depl...
Documentation as a governance control refers to the systematic recording of processes, decisions, and data related to AI systems. It is crucial in AI governance because it ensures...
An AI incident refers to any event where an AI system behaves unexpectedly, causes harm, or fails to comply with established guidelines and regulations. This concept is crucial in...
Enforcement in AI governance refers to the mechanisms and processes used to ensure compliance with established AI regulations, standards, and ethical guidelines. It is crucial for...
Regulatory sandboxes are controlled environments established by regulators that allow businesses to test innovative AI technologies and applications under a framework of oversight....
Monitoring in AI governance refers to the systematic observation and evaluation of AI systems to ensure they operate as intended, comply with regulations, and align with ethical st...
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