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Output attribution, content watermarking, and AI detection mechanisms.
Also in Non-Model
Identify what content is and is not from AI systems. Some methods also identify the originating AI system or even user.
Reasoning
Technical mechanism detects and attributes AI-generated content; identifies originating system or user through output analysis.
AI-content watermarking
1.2.5 Provenance & WatermarkingHuman-content watermarking
Similar to watermarking AI outputs, some systems may be able to watermark human-generated content.
1.2.5 Provenance & WatermarkingHash databases and perceptual hashing
Hash functions are one-way functions that usually take arbitrary inputs (like some AI-generated content) and output a short string that represents that content. For example, hash(<some image>) = abcd.
1.2.5 Provenance & WatermarkingContent provenance
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).
1.2.5 Provenance & WatermarkingContent classifiers
Content classifiers aim to directly identify existing AI content without special changes to the AI content itself. Usually, these are AI systems themselves, trained to distinguish between real and AI images (similar to a discriminator in a GAN).
1.2.5 Provenance & WatermarkingCompute 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
Developer
Entity that creates, trains, or modifies the AI system
Map
Identifying and documenting AI risks, contexts, and impacts
