Algorithmic Impact Assessment
intermediateSystematic evaluation of an AI system's potential effects on individuals, groups, and society before deployment. Required by some regulations, it identifies and mitigates potential harms.
Overview
Algorithmic Impact Assessments (AIAs) evaluate AI systems before deployment to identify potential negative effects. Similar to environmental impact assessments, they ensure organizations consider consequences before acting. AIAs typically examine: potential for discrimination or bias, effects on privacy and autonomy, impacts on different stakeholder groups, risks of misuse or failure, and societal implications at scale. Some jurisdictions now require AIAs for certain AI applications. Even where not mandated, they represent responsible development practice and can identify issues before they become costly problems.
Key Concepts
Stakeholder Analysis
Identifying all groups affected by the AI system.
Harm Identification
Systematically cataloging potential negative effects.
Mitigation Planning
Developing strategies to address identified risks.
Ongoing Monitoring
Tracking actual impacts after deployment.