[2606.29657] Safety from Honesty in a Disinterested AI Predictor

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Computer Science > Artificial Intelligence

arXiv:2606.29657 (cs)

[Submitted on 28 Jun 2026]

Title:Safety from Honesty in a Disinterested AI Predictor

Authors:Yoshua Bengio, Oliver Richardson, Tomáš Gavenčiak, Michael Cohen, Rory Svarc, Damiano Fornasiere, Gael Gendron, David Hyland, Aton Kamanda, Adam Oberman, Francis Rhys Ward, Anna Gavenčiak, Jacob Livingston Slosser, Vincent Mai, Iulian Serban, Joumana Ghosn<br>View a PDF of the paper titled Safety from Honesty in a Disinterested AI Predictor, by Yoshua Bengio and 15 other authors

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Abstract:As AI systems become more capable, training procedures that optimize for downstream outcomes risk introducing implicit agency: goal-directed behavior that designers never specified. We present a formal safety argument for the Scientist AI (SAI) Predictor, trained to approximate the Bayesian posterior conditioned on a dataset of "epistemically contextualized" natural-language statements. We argue that such a Predictor can honestly predict agents, actions, and their consequences without itself being an agent that selects outputs to achieve goals. This rests on data representation and on the training procedure. Epistemic contextualization of text distinguishes latent factual claims from communication acts, so expressions of goals are treated as evidence to be explained rather than drives the model adopts. With a posterior-seeking training objective, this is intended to drive the Predictor toward calibrated, cautious predictions. Training proceeds so downstream effects of deploying a prediction never serve as a reward signal; any agency the system needs is supplied by explicit scaffolding constrained by guardrails. We prove that, under assumptions on the training dynamics and on the argued sparsity of dangerous Predictors, the probability that training produces a Predictor whose guarded deployment carries residual harm above a specified threshold is small: a dangerous Predictor would have to underestimate harm in a coordinated way across many queries while such coordinated patterns are rare under the initialization distribution and receive no direct training signal. Safety and accuracy are jointly supported in this framework, since the constraints that secure accuracy are the same ones that make coordinated deception costly. These guarantees against misalignment and agency arising from within the Predictor itself do not preclude the use of the Predictor as part of an agentic system.

Subjects:

Artificial Intelligence (cs.AI); Machine Learning (cs.LG)

Cite as:<br>arXiv:2606.29657 [cs.AI]

(or<br>arXiv:2606.29657v1 [cs.AI] for this version)

https://doi.org/10.48550/arXiv.2606.29657

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arXiv-issued DOI via DataCite

Submission history<br>From: Yoshua Bengio [view email]<br>[v1]<br>Sun, 28 Jun 2026 23:55:29 UTC (63 KB)

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