A-Z Index
Browse concept cards whose titles begin with T. This is useful when you want an alphabetical view of the library rather than browsing by governance topic or category.
Traceability across the AI lifecycle refers to the ability to track and document the development, deployment, and performance of AI systems throughout their entire lifecycle. This...
Transparency as a governance principle in AI refers to the clear communication of how AI systems operate, including their decision-making processes, data usage, and potential biase...
Rule-Based Machine Learning (ML) Generative systems are AI models that operate based on predefined rules and logic to generate outputs. These systems rely on explicit programming t...
Tracking and Responding to Global AI Regulatory Developments involves monitoring and adapting to changes in AI laws and regulations across different jurisdictions. This is crucial...
Types of AI-related legal cases encompass various legal disputes arising from the deployment and use of artificial intelligence technologies. These cases can involve issues such as...
The trade-offs between fairness, accuracy, and utility in AI governance refer to the challenges of optimizing these three competing objectives when designing AI systems. Fairness a...
Training data refers to the dataset used to train an AI model, while operational data is the real-time data the model encounters during its deployment. In AI governance, distinguis...
Types of AI Governance Documentation refer to the various forms of records and guidelines that organizations create to manage AI systems effectively. This includes policies, proced...
Types of Impact Assessments, including Data Protection Impact Assessments (DPIA), Algorithmic Impact Assessments (AIA), and Hybrid assessments, are frameworks used to evaluate the...
Transparency trade-offs in AI governance refer to the balance between providing clear, understandable information about AI systems and the inherent complexity and risks associated...
Transparency in AI refers to the degree to which the processes and decisions of an AI system are open and accessible to stakeholders, while explainability pertains to the ability t...
Triggers for Regulatory Intervention refer to specific conditions or events that prompt regulatory bodies to take action against AI systems or their operators. These triggers are c...
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