Towards AI epidemiology: a measurement standardisation framework for prospective risk detection
arXiv:2512.15783v3 Announce Type: replace-cross Abstract: This paper proposes a measurement standardisation framework that compresses expert-AI interactions into structured, comparable fields for prospective risk detection in deployed AI systems, without access to model internals. The main aim of this concept paper is to define the scope of the framework, both semantically and statistically, and to specify a protocol for its empirical testing in future work. The population-level claims the framework is designed to support are therefore the subject of a staged research programme rather than res
The proliferation and increasing deployment of AI systems across critical infrastructure and societal functions necessitates robust frameworks for risk detection without relying on often inaccessible model internals.
This framework offers a path to standardise the assessment of AI risks, moving towards a more systematic and scalable approach to monitoring and governing deployed AI, crucial for maintaining trust and stability.
The capability to prospectively detect risks in AI systems, even without direct model access, becomes feasible through a standardised measurement framework, enabling proactive instead of reactive risk management.
- · AI governance bodies
- · Organizations deploying AI
- · AI auditors and risk managers
- · AI developers resistant to external scrutiny
- · Ad-hoc AI risk assessment methods
Improved safety and reliability of deployed AI systems through early risk detection.
Potential for new regulatory standards and compliance requirements based on standardised risk measurement.
Enhanced public trust in AI applications as risks become more transparently managed and mitigated.
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Read at arXiv cs.LG