Category Index
Browse every concept card currently tagged under Impact Assessments. Use this page to understand how this topic cluster appears across AI governance practice, then open individual concept cards for the details.
Core components of an AI Impact Assessment (AIA) include identifying potential risks, evaluating ethical implications, assessing societal impacts, and ensuring compliance with lega...
Documenting Intended Purpose and Context involves clearly articulating the objectives and operational environment for which an AI system is designed. This practice is crucial in AI...
AI Impact Assessments (AIAs) are systematic evaluations that analyze the potential effects of AI systems on individuals, society, and the environment. They are crucial in AI govern...
Risk identification within impact assessments refers to the systematic process of recognizing potential risks associated with AI systems before they are deployed. This concept is c...
Impact assessments in high-risk AI governance are systematic evaluations that analyze the potential effects of AI systems on individuals and society before their deployment. These...
Types of Impact Assessments, including Data Protection Impact Assessments (DPIA), Algorithmic Impact Assessments (AIA), and Hybrid assessments, are frameworks used to evaluate the...
Using Impact Assessments as Assurance Evidence involves systematically evaluating the potential effects of AI systems on individuals and society before deployment. This process is...
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...
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