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Output attribution, content watermarking, and AI detection mechanisms.
Also in Non-Model
Similar to watermarking AI outputs, some systems may be able to watermark human-generated content.
The key difficulty here is that determining something has human origin is quite difficult. There are a few approaches that work in specific cases: - For text content, some schemes have been proposed for recording the user’s keystrokes and writing process, and then using this to certify it was written by a human. This might be broken if the system is trained to imitate how humans type. - For image or video content, secure element chips could be used in cameras that certify content was recorded with a real camera. However, given adversaries would have unlimited access to this hardware, a well-resourced adversary could likely break this scheme. Additionally, it’s hard to tell the difference between a real photo, and a real photo of a screen showing a fake photo.
Reasoning
Watermarking and certification systems enable attribution of human-generated content and distinguish from AI-generated outputs.
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.
Other (outside lifecycle)
Outside the standard AI system lifecycle
Governance Actor
Regulator, standards body, or oversight entity shaping AI policy
Manage
Prioritising, responding to, and mitigating AI risks
