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Structured analysis to identify, characterize, and prioritize potential harms and risks.
Also in Risk & Assurance
Cross impact analysis is a forecasting methodology that analyzes the likelihood of a particular issue using expert analysis (i.e., Delphi technique) in combination with analysis of events correlated with the said issue. It involves decomposing an issue into discrete and correlated events, and then collecting expert opinion on each of those events. Analysis of each event from multiple viewpoints can yield potential future scenarios [107]
For example, an issue may be “advances in AI,” which can be broken down into two correlated events like “advances in hardware” or “advances in algorithms,” where the likelihood of occurrences of each event can be estimated via the Delphi technique while taking into account their interactions with other events.
Reasoning
Analyzes potential harms across system impacts to identify and prioritize risks pre-deployment.
Risk Assessment
Model development
2.4 Engineering & DevelopmentModel development > Data-related
1.1 ModelModel evaluations
2.2.2 Testing & EvaluationModel evaluations > General evaluations
2.2.2 Testing & EvaluationModel evaluations > Benchmarking
3.2.1 Benchmarks & EvaluationModel evaluations > Red teaming
2.2.2 Testing & EvaluationRisk Sources and Risk Management Measures in Support of Standards for General-Purpose AI Systems
Gipiškis, Rokas; San Joaquin, Ayrton; Chin, Ze Shen; Regenfuß, Adrian; Gil, Ariel; Holtman, Koen (2024)
Organizations and governments that develop, deploy, use, and govern AI must coordinate on effective risk mitigation. However, the landscape of AI risk mitigation frameworks is fragmented, uses inconsistent terminology, and has gaps in coverage. This paper introduces a preliminary AI Risk Mitigation Taxonomy to organize AI risk mitigations and provide a common frame of reference. The Taxonomy was developed through a rapid evidence scan of 13 AI risk mitigation frameworks published between 2023-2025, which were extracted into a living database of 831 distinct AI risk mitigations.
Plan and Design
Designing the AI system, defining requirements, and planning development
Governance Actor
Regulator, standards body, or oversight entity shaping AI policy
Map
Identifying and documenting AI risks, contexts, and impacts
