Evaluation Frameworks

Evaluation frameworks are structured methodologies used to assess the performance, safety, and fairness of AI systems across defined metrics and criteria. For enterprise AI governance, these frameworks are critical because they enable organizations to measure whether AI models meet regulatory requirements, business objectives, and ethical standards before and after deployment. Common evaluation approaches include technical benchmarks, bias audits, explainability assessments, and real-world performance monitoring, all of which help governance teams document compliance and identify risks systematically.

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ResearchGlobal2026-09-15

IBM Study: AI Models Miss Physical Harm Risk When Flying Drones

IBM has summarized new research showing that AI models can generate code to operate a simulated drone while failing to account for crash risk or harm to people. The study identifies a structural gap between software-benchmark performance and physical-world safety. The findings carry direct implications for deployment approval and safety evaluation programs for any AI system that interacts with the physical environment.

IBM AI safety autonomous systems drone governance physical harm pre-deployment testing evaluation frameworks