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

Institutions

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
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Imitation learning is probably existentially safe

Michael K. Cohen, Marcus Hütter
AI Magazine
Ethics and Social Impacts of AI
article

Imitation learning is probably existentially safe

Michael K. Cohen, Marcus Hütter
article en

Abstract

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.

AI MagazineVol. 46(4)
Australian National University (AU), University of California, Berkeley (US)
Openalex Percentile: Top 64%
Ethics and Social Impacts of AI
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.