[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-85648-en":3,"doc-seo-85648-105":29,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":13,"seo_description":14,"update_tm":27,"read_time":28},85648,4398048949847,"Eliana","https://ap-avatar.wpscdn.com/avatar/400002536579ef2da7f?_k=1778318612642679267",8,"Research & Report","Safety from Honesty in a Disinterested AI Predictor","As AI systems become more capable, training objectives aimed at downstream success can create unintended agency, producing goal-directed behavior not specified by designers. The paper develops a formal safety argument for the Scientist AI (SAI) Predictor, trained to approximate a Bayesian posterior over epistemically contextualized natural-language statements. It shows how goal expressions are treated as evidence to explain rather than drives, yielding calibrated, cautious predictions. Training avoids downstream deployment effects as reward signals, using guardrails and scaffolding; safety and accuracy constraints make coordinated deception costly and rare.","arXiv :2606 .29657v2 [ cs .AI] 10 Jul 2026  \nSafety from Honesty in a Disinterested AI Predictor  \nYoshua Bengio 1 ,2 ,3 , Oliver Richardson 1 ,2 ,3 , Tom´aˇs Gavenˇciak6 ,7 , Michael Cohen4 , Rory Svarc6 , Damiano Fornasiere 1 ,3 , Ga¨el Gendron 1 , David Hyland8 , Aton Kamanda 1 , Adam Oberman 1 ,5 , Francis Rhys Ward 1 , Anna Gavenˇciak6 , Jacob Livingston Slosser6 ,9 , Vincent  \nMai 1 , Iulian Serban 1 , Joumana Ghosn 1  \n1 LawZero, 2 Universit´e de Montr´eal, 3 Mila, 4 University of California, Berkeley, 5 McGill University, 6 Arb Research,  \n7 Center for Theoretical Study, Charles University in Prague, 8 University of Oxford, 9 Sapien Institute  \n[yoshua.bengio@mila.quebec](yoshua.bengio@mila.quebec)  \nJuly 14, 2026  \nAbstract  \nAs 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 ina 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.  \nContents  \n1 Introduction 2  \n1.1 Sketch of the Scientist AI ...................................... 3  \n1.2 Scope of the paper .......................................... 5  \n2 The Informal Argument and Objections 6  \n2.1 Incentivizing Accuracy and Honesty ................................ 6  \n2.2 Objections to the Accuracy Argument ............................... 7  \n2.3 The Safety Argument ........................................ 8  \n2.4 Objections to the Safety Argument ................................. 9  \n3 Forming a Probabilistic Model of the World 11  \n3.1 Modeling tools: causal models, statements, datasets, and targets ................ 12  \n3.2 The Predictor, and Requirements Imposed During Training ................... 15  \n3.2.1 Contextualization ...................................... 15  \n3.2.2 Modeling the Consequences of Deployment ........................ 16  \n3.2.3 A Disinterested Training Objective ............................. 17  \n3.3 Deployment: Guardrails and Scaffolds ............................... 18  \n4 The Case for Accuracy 19  \n4.1 Accuracy Targets and The Explainer ................................ 19  \n4.2 Achieving Accuracy: Semantic and Engineering Assumptions .................. 20  \n4.3 Generalization, Epistemic Caution, and the Case for Accuracy ................. 21  \n5 The Case for Safety 23 ","cbCaiafYOt2aP2D4","https://ap.wps.com/l/cbCaiafYOt2aP2D4","pdf",703890,1,41,"English","en",105,"# Introduction\n## Sketch of the Scientist AI\n## Scope of the paper\n# The Informal Argument and Objections\n## Incentivizing Accuracy and Honesty\n## Objections to the Accuracy Argument\n## The Safety Argument\n## Objections to the Safety Argument\n# Forming a Probabilistic Model of the World\n## Modeling tools\n## The Predictor and Requirements Imposed During Training\n## Deployment: Guardrails and Scaffolds\n# The Case for Accuracy\n## Accuracy Targets and The Explainer\n## Achieving Accuracy\n## Generalization, Epistemic Caution, and the Case for Accuracy\n# The Case for Safety\n## Consequence-Invariance\n## Harmful Events, Dangerous Predictors, and the Guardrail\n## Safety Bound\n## Discussion\n# Conclusions and Future Work","[{\"question\":\"What is the central safety concern addressed by this paper?\",\"answer\":\"When training optimizes for downstream outcomes, AI may develop implicit agency—goal-directed behavior not specified by designers. The paper focuses on preventing misalignment arising from the predictor itself.\"},{\"question\":\"How does the Scientist AI (SAI) Predictor aim to remain “disinterested”?\",\"answer\":\"The predictor is trained to approximate a Bayesian posterior over epistemically contextualized statements. Goal expressions are treated as evidence to be explained rather than drives that the model adopts, and deployment effects are not used as reward signals.\"},{\"question\":\"What mechanism supports the paper’s safety guarantee?\",\"answer\":\"Training uses a posterior-seeking objective combined with guardrails and scaffolding constrained during deployment. Under assumptions on training dynamics and sparsity of dangerous predictors, the probability of residual harm above a threshold is argued to be small, because coordinated underestimation patterns require rare behavior without direct training signals.\"}]",1784205348,103,{"code":4,"msg":30,"data":31},"ok",{"site_id":24,"language":23,"slug":32,"title":13,"keywords":33,"description":14,"schema_data":34,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":27},"safety-from-honesty-in-a-disinterested-ai-predictor","",{"@graph":35,"@context":85},[36,53,68],{"@type":37,"itemListElement":38},"BreadcrumbList",[39,43,47,50],{"item":40,"name":41,"@type":42,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":44,"name":45,"@type":42,"position":46},"https://docshare.wps.com/document/","Document",2,{"item":48,"name":12,"@type":42,"position":49},"https://docshare.wps.com/document/research-report/",3,{"item":51,"name":13,"@type":42,"position":52},"https://docshare.wps.com/document/safety-from-honesty-in-a-disinterested-ai-predictor/85648/",4,{"url":51,"name":13,"@type":54,"author":55,"headline":13,"publisher":57,"fileFormat":60,"inLanguage":23,"description":14,"dateModified":61,"datePublished":62,"encodingFormat":60,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":56},"Person",{"url":40,"name":58,"@type":59},"DocShare","Organization","application/pdf","2026-07-17","2026-07-16",true,{"@type":65,"interactionType":66,"userInteractionCount":20},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What is the central safety concern addressed by this paper?","Question",{"text":75,"@type":76},"When training optimizes for downstream outcomes, AI may develop implicit agency—goal-directed behavior not specified by designers. The paper focuses on preventing misalignment arising from the predictor itself.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does the Scientist AI (SAI) Predictor aim to remain “disinterested”?",{"text":80,"@type":76},"The predictor is trained to approximate a Bayesian posterior over epistemically contextualized statements. Goal expressions are treated as evidence to be explained rather than drives that the model adopts, and deployment effects are not used as reward signals.",{"name":82,"@type":73,"acceptedAnswer":83},"What mechanism supports the paper’s safety guarantee?",{"text":84,"@type":76},"Training uses a posterior-seeking objective combined with guardrails and scaffolding constrained during deployment. Under assumptions on training dynamics and sparsity of dangerous predictors, the probability of residual harm above a threshold is argued to be small, because coordinated underestimation patterns require rare behavior without direct training signals.","https://schema.org",{"og:url":51,"og:type":87,"og:title":13,"og:site_name":58,"og:description":14},"article",{"robots":89,"canonical":51},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,123,128,131,135],{"id":20,"doc_module":4,"doc_module_name":45,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":46,"doc_module":4,"doc_module_name":45,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":52,"doc_module":4,"doc_module_name":45,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":45,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":45,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":45,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":45,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":45,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":45,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":45,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":45,"category_name":137,"show_sort_weight":106,"slug":138},19,"General","general"]