A-Z Index
Browse concept cards whose titles begin with L. This is useful when you want an alphabetical view of the library rather than browsing by governance topic or category.
Lifecycle Coverage in Compliance Frameworks refers to the comprehensive integration of compliance measures throughout the entire lifecycle of AI systems, from development and deplo...
Lifecycle Thinking in AI Regulation refers to the approach of considering the entire lifecycle of an AI system—from design and development to deployment, operation, and decommissio...
The limits of existing AI governance frameworks refer to the inadequacies and gaps in current regulations and guidelines that fail to address the rapid evolution of AI technologies...
The lawful basis for processing personal data refers to the legal grounds under which organizations can collect, store, and use individuals' personal information. In AI governance,...
Lessons learned from AI governance failures refer to insights gained from past incidents where AI systems have caused harm or operated outside ethical and legal boundaries. These f...
Lifecycle Obligations Triggered by High-Risk Classification refer to the regulatory requirements that arise when an AI system is classified as high-risk due to its potential impact...
Limited-risk AI systems are those that pose a moderate risk to rights and safety, requiring specific transparency obligations under AI governance frameworks. These obligations mand...
Local Adaptation vs Global Standardisation refers to the balance between tailoring AI governance frameworks to local contexts and adhering to universal standards. In AI governance,...
Likelihood vs Impact in AI governance refers to a risk assessment framework that evaluates potential risks based on two dimensions: the probability of an adverse event occurring (l...
Learning and Evidence Generation from Sandboxes refers to the practice of using regulatory sandboxes—controlled environments where AI technologies can be tested under real-world co...
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