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Output attribution, content watermarking, and AI detection mechanisms.
Also in Non-Model
Content provenance (also known as ‘chain of custody’) focuses on recording how content has been created and updated over time. This provides much more detailed information than other methods (which are usually a more binary yes/no for being AI-generated).
This stores detailed metadata about how an image has been created and modified. The first step in this chain might be [AI-content](https://adamjones.me/blog/ai-regulator-toolbox/#ai-content-watermarking) or [human-content](https://adamjones.me/blog/ai-regulator-toolbox/#human-content-watermarking) watermarking. For a single image, this metadata might look something like: - Image was created using OpenAI’s DALL·E 3 by user with id ‘e096’ using prompt ‘A cabin in the woods’ (signed by OpenAI) - Image was edited using Adobe Express by user with id ‘4b20’, using the text and colour filter tools (signed by Adobe) - Image was resized and compressed by Google when it was attached to a Gmail message (signed by Google)
Reasoning
Records content creation and modification history with cryptographic signatures for output attribution tracking.
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
Governance Actor
Regulator, standards body, or oversight entity shaping AI policy
Manage
Prioritising, responding to, and mitigating AI risks
