[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"branding":3,"analytics":7,"article-new-study-shows-limits-of-rule-based-auditing-for-ai":10,"sections":34},{"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":29,"feedback":33,"feedback_at":22,"cost_usd":33,"total_tokens":33},7920,"new-study-shows-limits-of-rule-based-auditing-for-ai","New Study Shows Limits of Rule-Based Auditing for AI","A new arXiv paper tests whether AI policies can be audited through shared symbolic rules, and finds rule overlap doesn't guarantee matching behavior.","Turns out you can't audit a reinforcement-learning policy just by checking whether its rules look like another one's.\n\nResearchers built a pipeline that translates opaque reinforcement-learning policies - the trained models that make decisions in games or control tasks - into human-readable, if-this-then-that rules, then tried fusing rules from separately trained policies into one combined policy. They tested the approach across two environments and split auditability into six separately testable checks, covering things like whether extracted rules match recorded actions, whether they cover enough situations, and whether merged rule sets behave sensibly. The core finding: two policies can share nearly identical rule sets and still choose close-to-random, mismatched actions on states they haven't seen before. In one task, an apparent fusion failure turned out to be a bookkeeping error - rules were extracted from sampled actions but tested against a different decision method, and fixing that mismatch flipped the result.\n\nThis lands right as interpretability tools get pitched as a fix for black-box AI, the promise being that a compact rulebook can stand in for a neural network during oversight or certification. This paper is a caution flag: rule extraction can look tidy on paper while failing to predict what the underlying policy actually does when it matters, which undercuts the basic premise of using extracted rules as a stand-in for the real model.\n\nOne comparison did favor the rule-based approach, but the researchers themselves note the comparator was chosen after the fact, the task was largely solved already, and the fused policy still trailed the best plain neural policy - which is a pretty modest win to hang an auditing framework on.","[\"ai\",\"reinforcement-learning\",\"interpretability\",\"machine-learning\"]","2026-09-25T04:00:00.000Z","2026-09-26T07:05:40.569Z","2026-09-26T07:05:45.326Z","published",null,[],"ai",[24,26,27,28],"reinforcement-learning","interpretability","machine-learning",[30],{"name":31,"url":32},"arXiv cs.AI","https:\u002F\u002Farxiv.org\u002Fabs\u002F2609.28581",0,{"sections":35},[36,40,45,50,55,60,64,69,74,79,84,89,94,99],{"name":37,"slug":24,"count":38,"latest_published_at":39},"AI",4624,"2026-09-25T21:57:05.000Z",{"name":41,"slug":42,"count":43,"latest_published_at":44},"Security","security",748,"2026-09-26T01:30:00.000Z",{"name":46,"slug":47,"count":48,"latest_published_at":49},"Policy","policy",392,"2026-09-25T18:44:30.000Z",{"name":51,"slug":52,"count":53,"latest_published_at":54},"Deals","deals",258,"2026-09-26T09:00:00.000Z",{"name":56,"slug":57,"count":58,"latest_published_at":59},"Hardware","hardware",185,"2026-09-25T15:00:22.000Z",{"name":61,"slug":62,"count":63,"latest_published_at":54},"Science","science",144,{"name":65,"slug":66,"count":67,"latest_published_at":68},"Consumer Tech","consumer-tech",133,"2026-09-26T07:30:06.000Z",{"name":70,"slug":71,"count":72,"latest_published_at":73},"Software","software",90,"2026-09-25T20:55:00.000Z",{"name":75,"slug":76,"count":77,"latest_published_at":78},"Dev Tools","dev-tools",84,"2026-09-26T04:20:58.000Z",{"name":80,"slug":81,"count":82,"latest_published_at":83},"Startups","startups",76,"2026-09-25T18:33:59.000Z",{"name":85,"slug":86,"count":87,"latest_published_at":88},"Gaming","gaming",48,"2026-09-25T18:35:21.000Z",{"name":90,"slug":91,"count":92,"latest_published_at":93},"General","general",46,"2026-09-25T02:12:57.000Z",{"name":95,"slug":96,"count":97,"latest_published_at":98},"Reviews","reviews",30,"2026-09-24T20:07:31.000Z",{"name":100,"slug":101,"count":102,"latest_published_at":103},"How-To","how-to",6,"2026-06-16T09:00:00.000Z"]