[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"branding":3,"analytics":7,"article-new-research-maps-the-blind-spots-in-human-ai-oversight":10,"sections":35},{"siteName":4,"siteTagline":5,"publisherName":4,"contactEmail":6},"The Revision","Tech news, decoded.","editor@therevision.news",{"gaMeasurementId":8,"adsenseClientId":9},"G-ZW2MV82GYR","ca-pub-8533917693782264",{"article":11},{"id":12,"slug":13,"title":14,"dek":15,"body_md":16,"tags_json":17,"published_at":18,"created_at":19,"updated_at":20,"status":21,"review_note":22,"review_notes":23,"image_url":22,"persona_id":22,"persona_name":22,"section":24,"tags":25,"sources":30,"feedback":34,"feedback_at":22,"cost_usd":34,"total_tokens":34},3186,"new-research-maps-the-blind-spots-in-human-ai-oversight","New Research Maps the Blind Spots in Human AI Oversight","A new paper models what happens when an AI knows something is wrong but its human supervisor doesn't — and finds a measurable gap where harm slips through.","A formal model of human-AI oversight finds a specific, quantifiable region where an AI agent knows its proposed action is harmful but a trusting human lets it proceed anyway.\n\nResearchers built what they call a contextual-bandit team game — a mathematical framework that strips out physical state changes to get clean, exact results. In the setup, the human privately knows what outcomes she wants, while the AI privately knows how good its own proposed action actually is. Using that two-sided information gap, the paper identifies two equilibria: a team optimum (where things go well) and a myopic rule (where the human trusts her prior beliefs and skips oversight). The space between those two outcomes is what the authors call \"avoidable harm\" — situations where shutdown would have helped but didn't happen.\n\nThe finding matters because it gives AI safety researchers a precise target rather than a vague worry. Most oversight discussions stay qualitative; this one produces a slab-shaped gap in a decision space that can, in principle, be measured and reduced. The paper also traces how repeated interactions — through passive learning and active signaling — gradually close that gap, which is a more realistic model than single-shot safety analyses.\n\nThe framework builds on Cooperative Inverse Reinforcement Learning, a decade-old research thread, and the authors are candid that their bandit simplification sidesteps the harder partially observable Markov problem. Whether the clean math survives contact with a real autonomous system is a question the paper leaves open.","[\"ai safety\",\"research\",\"human oversight\",\"reinforcement learning\"]","2026-07-02T04:00:00.000Z","2026-07-02T04:09:25.521Z","2026-07-02T04:09:28.527Z","published",null,[],"ai",[26,27,28,29],"ai safety","research","human oversight","reinforcement learning",[31],{"name":32,"url":33},"arXiv cs.AI","https:\u002F\u002Farxiv.org\u002Fabs\u002F2607.00155",0,{"sections":36},[37,41,46,51,56,61,66,71,76,81,86,90,95,100],{"name":38,"slug":24,"count":39,"latest_published_at":40},"AI",2590,"2026-07-16T04:00:00.000Z",{"name":42,"slug":43,"count":44,"latest_published_at":45},"Security","security",294,"2026-07-15T19:59:48.000Z",{"name":47,"slug":48,"count":49,"latest_published_at":50},"Deals","deals",179,"2026-06-29T20:02:07.000Z",{"name":52,"slug":53,"count":54,"latest_published_at":55},"Policy","policy",158,"2026-07-16T00:02:48.000Z",{"name":57,"slug":58,"count":59,"latest_published_at":60},"Hardware","hardware",122,"2026-07-14T19:46:26.000Z",{"name":62,"slug":63,"count":64,"latest_published_at":65},"Consumer Tech","consumer-tech",93,"2026-07-13T13:20:48.000Z",{"name":67,"slug":68,"count":69,"latest_published_at":70},"Software","software",70,"2026-07-13T19:52:25.000Z",{"name":72,"slug":73,"count":74,"latest_published_at":75},"Science","science",66,"2026-07-10T10:29:37.000Z",{"name":77,"slug":78,"count":79,"latest_published_at":80},"Dev Tools","dev-tools",59,"2026-07-07T04:00:00.000Z",{"name":82,"slug":83,"count":84,"latest_published_at":85},"Gaming","gaming",41,"2026-07-09T04:00:00.000Z",{"name":87,"slug":88,"count":84,"latest_published_at":89},"Startups","startups","2026-06-29T20:55:50.000Z",{"name":91,"slug":92,"count":93,"latest_published_at":94},"General","general",29,"2026-07-10T22:28:58.000Z",{"name":96,"slug":97,"count":98,"latest_published_at":99},"Reviews","reviews",20,"2026-06-24T12:00:01.000Z",{"name":101,"slug":102,"count":103,"latest_published_at":104},"How-To","how-to",6,"2026-06-16T09:00:00.000Z"]