AI systems can cause harm through biased decisions, safety failures, privacy violations, and security vulnerabilities. Without pre-deployment risk assessment, organizations discover these harms through incidents rather than analysis.
Each AI system undergoes risk assessment proportional to its classification tier. Assessment evaluates fairness and bias, data quality and governance, accuracy and reliability, security and adversarial robustness, transparency and explainability, and human oversight adequacy. Fundamental rights impact assessments (FRIA) are mandatory under the EU AI Act for high-risk deployers. Risk treatment plans define mitigations, residual risk acceptance, and ongoing monitoring requirements.
AI risk assessment platforms, bias testing frameworks, FRIA templates, and model validation suites.
Systematic detection, measurement, and mitigation of algorithmic bias across protected characteristics to ensure equitable AI outcomes.
Systematic testing of AI systems for adversarial robustness, edge-case failures, hallucination rates, and safety-critical failure modes before and.
Techniques and documentation enabling humans to understand how AI systems reach their outputs — from model architecture through decision rationale.
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