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Runtime monitoring, observability, performance tracking, and anomaly detection in production.
Also in Operations & Security
Monitor for harmful applications of AI systems by analysing patterns of behaviour.
Work to be done here includes: 1. Developing algorithms to detect potentially harmful patterns in how AI systems are being used. Alternatively, developing guidelines/best practices on doing so. 2. Developing a shared register of known actors who demonstrate suspicious behaviour (similar to [Cifas](https://www.cifas.org.uk/) in financial services). 3. Coordinating monitoring across different AI providers to detect distributed abuse attempts. 4. Establishing clear procedures for how to respond to detected abuses, including AI companies escalating to law enforcement, and guidance on how law enforcement should respond. 5. Proposing regulations that would support the above.
Reasoning
Coordinating abuse monitoring across AI providers to detect distributed misuse patterns requires cross-organization information sharing and collaborative detection mechanisms.
Compute goverance
Regulate companies in the highly concentrated AI chip supply chain, given AI chips are key inputs to developing frontier AI models.
3.1.1 Legislation & PolicyData input controls
Filter data used to train AI models, e.g. don’t train your model with instructions to launch cyberattacks.
1.1.1 Training DataLicensing
Require organisations or specific training runs to be licensed by a regulatory body, similar to licensing regimes in other high-risk industries.
3.1.4 Compliance RequirementsOn-chip governance mechanisms
Make alterations to AI hardware (primarily AI chips), that enable verifying or controlling the usage of this hardware.
1.2.4 Security InfrastructureSafety cases
Develop structured arguments demonstrating that an AI system is unlikely to cause catastrophic harm, to inform decisions about training and deployment.
2.2.4 Assurance DocumentationEvaluations (aka “evals”)
Give AI systems standardised tests to assess their capabilities, which can inform the risks they might pose.
2.2.2 Testing & EvaluationThe AI regulator’s toolbox: A list of concrete AI governance practices
Jones, Adam (2024)
This article explains concrete AI governance practices people are exploring as of August 2024. Prior summaries have mapped out high-level areas of work, but rarely dive into concrete practice details. This summary explores specific practices addressing risks from advanced AI systems. Practices are grouped into categories based on where in the AI lifecycle they best fit. The primary goal of this article is to help newcomers contribute to the field of AI governance by providing a comprehensive overview of available practices.
Operate and Monitor
Running, maintaining, and monitoring the AI system post-deployment
Deployer
Entity that integrates and deploys the AI system for end users
Measure
Quantifying, testing, and monitoring identified AI risks
