GCRI · Fields across the Consortium
Artificial intelligence
AI risk management asks what a system can do, how it fails, who is affected and who is accountable. The lifecycle matters: capability can change after deployment, and harms can be diffuse.
01Contents
Artificial intelligence in six parts.
02Three mandates
The field, seen three ways.
03The systemic question
From model failure to systemic effect.
Individual AI failures (biased outputs, hallucinated facts, unsafe actions by an agent) are well catalogued. Systemic effects arise when many actors use similar models trained on similar data, when AI is embedded in critical decisions such as credit or triage, or when capability shifts faster than oversight. Assessment has to cover the model, the system it sits in and the institution operating it.
| Layer | Typical risk | Assessment |
|---|---|---|
| Model | Robustness, bias, capability for misuse | Evaluations and red teaming |
| System | Integration errors, over-reliance, data leakage | Impact assessment, human oversight design |
| Institution | Unclear accountability, missing incident response | Management system audit (ISO/IEC 42001) |
| Ecosystem | Homogeneity, concentration, correlated failure | Market and dependency analysis |
04Frameworks
Risk frameworks and law.
| Framework or body | What it governs or supplies |
|---|---|
| NIST AI RMF and its generative AI profile | Govern, map, measure and manage functions |
| ISO/IEC 42001 and ISO/IEC 23894 | AI management systems and AI risk management guidance |
| EU AI Act | Risk-tiered obligations applied in stages; check current dates for each obligation |
| OECD AI Principles and UNESCO Recommendation on the Ethics of AI | Intergovernmental principles |
05Couplings
Where AI risk lands.
- Compute: capability concentration and energy demand.
- Work: task exposure and transition.
- Information integrity: synthetic content and trust.
- Financial stability: model risk and herding.
The science sits on AI and Autonomous Systems; methods start from AI assurance.
06Grounding
The methods behind the field.
- Artificial intelligence and machine learning
- AI evaluation and assurance engineering
- High-performance, cloud and edge computing
- Probability and statistics
- Cybersecurity and cryptography
- Social and behavioural science
- Robotics and automation
- Economics and development economics
- Econometrics
- Policy, institutional and decision science
- Systems engineering
- Digital identity and public digital systems
- Uncertainty quantification and model validation
07Sources
Where this page’s references lead.
The bodies and documents this page is read against. Status and editions change; each link goes to the issuing body.
08Questions
Questions and answers.
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