Belief updating in AI‐risk debates: Exploring the limits of adversarial collaboration

We organized adversarial collaborations between subject-matter experts and expert forecasters with opposing views on whether recent advances in Artificial Intelligence (AI) pose an existential threat to humanity in the 21st century. Two studies incentivized participants to engage in respectful perspective-taking, to share their strongest arguments, and to propose early-warning indicator questions (cruxes) for the probability of an AI-related catastrophe by 2100. AI experts saw greater threats from AI than did expert forecasters, and neither group changed its long-term risk estimates, but they did preregister cruxes whose resolution by 2030 would sway their views on long-term risk. These persistent differences shrank as questioning moved across centuries, from 2100 to 2500 and beyond, by which time both groups put the risk of extreme negative outcomes from AI at 30%-40%. Future research should address the generalizability of these results beyond our sample to alternative samples of experts, and beyond the topic area of AI to other questions and time frames.

Authors

Institutions

Publication Details

Journal
Risk Analysis
Published
2025-04-03
DOI
https://doi.org/10.1111/risa.70023
Citations
1
Primary Topic
Risk Perception and Management
Type
article
Field-Weighted Citation Impact
4.20

Funders

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

Belief updating in AI‐risk debates: Exploring the limits of adversarial collaboration

Otto Kuusela, Molly Hickman, Ezra Karger, Josh Rosenberg et al.
1 citations
Risk Analysis
Risk Perception and Management
4.20
article

Belief updating in AI‐risk debates: Exploring the limits of adversarial collaboration

Otto Kuusela, Molly Hickman, Ezra Karger, Josh Rosenberg, Zach Jacobs, Philip E. Tetlock, Albert J. Morris, Harrison Durland
article en
1 citations

Abstract

We organized adversarial collaborations between subject-matter experts and expert forecasters with opposing views on whether recent advances in Artificial Intelligence (AI) pose an existential threat to humanity in the 21st century. Two studies incentivized participants to engage in respectful perspective-taking, to share their strongest arguments, and to propose early-warning indicator questions (cruxes) for the probability of an AI-related catastrophe by 2100. AI experts saw greater threats from AI than did expert forecasters, and neither group changed its long-term risk estimates, but they did preregister cruxes whose resolution by 2030 would sway their views on long-term risk. These persistent differences shrank as questioning moved across centuries, from 2100 to 2500 and beyond, by which time both groups put the risk of extreme negative outcomes from AI at 30%-40%. Future research should address the generalizability of these results beyond our sample to alternative samples of experts, and beyond the topic area of AI to other questions and time frames.

Risk AnalysisVol. 45(12)
Federal Reserve Bank of Chicago (US), University of Pennsylvania (US)
Musk Foundation
Openalex Percentile: Top 9%
Risk Perception and Management
4.20
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.

Belief updating in AI‐risk debates: Exploring the limits of adversarial collaboration — Otto Kuusela, Molly Hickman, et al. · Risk Analysis (2025) | TGRS Research Map | TGRS