Imitation learning is probably existentially safe
Abstract Concerns about extinction risk from AI vary among experts in the field. However, AI encompasses a very broad category of algorithms. Perhaps some algorithms would pose an extinction risk, and others would not. Such an observation might be of great interest to both regulators and innovators. This paper argues that advanced imitation learners would likely not cause human extinction. We first present a simple argument to that effect, and then we rebut six different arguments that have been made to the contrary. A common theme of most of these arguments is a story for how a subroutine within an advanced imitation learner could hijack the imitation learner's behavior toward its own ends. However, we argue that each argument is flawed and each story implausible.
Authors
- Michael K. Cohen (ORCID: https://orcid.org/0000-0003-1749-875X)
- Marcus Hütter (ORCID: https://orcid.org/0000-0002-3263-4097)
Institutions
- Australian National University (AU)
- University of California, Berkeley (US)
Publication Details
- Journal
- AI Magazine
- Published
- 2025-11-21
- DOI
- https://doi.org/10.1002/aaai.70040
- Primary Topic
- Ethics and Social Impacts of AI
- Type
- article
- Field-Weighted Citation Impact
- 0.00