IDs for AI Systems
<|reference_start|>IDs for AI Systems: AI systems are increasingly pervasive, yet information needed to decide whether and how to engage with them may not exist or be accessible. A user may not be able to verify whether a system has certain safety certifications. An investigator may not know whom to investigate when a system causes an incident. It may not be clear whom to contact to shut down a malfunctioning system. Across a number of domains, IDs address analogous problems by identifying particular entities (e.g., a particular Boeing 747) and providing information about other entities of the same class (e.g., some or all Boeing 747s). We propose a framework in which IDs are ascribed to instances of AI systems (e.g., a particular chat session with Claude 3), and associated information is accessible to parties seeking to interact with that system. We characterize IDs for AI systems, provide concrete examples where IDs could be useful, argue that there could be significant demand for IDs from key actors, analyze how those actors could incentivize ID adoption, explore a potential implementation of our framework for deployers of AI systems, and highlight limitations and risks. IDs seem most warranted in settings where AI systems could have a large impact upon the world, such as in making financial transactions or contacting real humans. With further study, IDs could help to manage a world where AI systems pervade society.<|reference_end|>
@article{chan2024ids,
    title={IDs for AI Systems},
    author={Alan Chan, Noam Kolt, Peter Wills, Usman Anwar, Christian Schroeder de
  Witt, Nitarshan Rajkumar, Lewis Hammond, David Krueger, Lennart Heim, Markus
  Anderljung},
    journal={arXiv preprint arXiv:2406.12137},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.12137},
    primaryClass={cs.AI}
}