{"data":{"event":{"id":"a67a83cd-ec23-4880-a76e-997b0104872b","slug":"from-legal-text-to-ai-specific-risk-sources-a-systematic-analysis-of-the-3047d5b007","title":"From Legal Text to AI-specific Risk Sources: A Systematic Analysis of the EU AI Act's High-Risk Requirements","short_summary":"arXiv:2609.13535v1 Announce Type: new \nAbstract: The EU AI Act introduces mandatory requirements for high-risk AI systems with the explicit goal of ensuring the development and operation of trustworthy AI. At the same time, AI risk management practices rely on structured risk taxonomies to systematically identify and treat AI-specific risk sources. As both the AI Act and established risk taxonomies aim to address AI-induced risks, a natural question is whether they align in the risk sources they cover. However, no clear mapping exists between the risks implicitly addressed by the Act's high-ri","full_description":"arXiv:2609.13535v1 Announce Type: new \nAbstract: The EU AI Act introduces mandatory requirements for high-risk AI systems with the explicit goal of ensuring the development and operation of trustworthy AI. At the same time, AI risk management practices rely on structured risk taxonomies to systematically identify and treat AI-specific risk sources. As both the AI Act and established risk taxonomies aim to address AI-induced risks, a natural question is whether they align in the risk sources they cover. However, no clear mapping exists between the risks implicitly addressed by the Act's high-risk requirements and established taxonomies, leaving practitioners without a structured basis for aligning regulatory obligations with AI risk management practice. This paper presents a systematic classification of the requirements extracted from the EU AI Act Section 2 (Requirements for high-risk AI systems), revealing that only a minority directly address AI-specific risk sources, while the majority impose organizational process and documentation obligations. From the AI risk-related requirements, a consolidated list of distinct AI-specific risk sources is derived. The resulting EU AI Act Risk Source List takes an important step towards bridging the gap between legal obligation and AI risk management practice, providing a structured reference for explicit comparison between existing AI risk taxonomies and the risk sources implicitly addressed by the EU AI Act. Important Note: This is the authors' preprint. The paper was presented at the 4th International Conference on Frontiers of Artificial Intelligence, Ethics, and Multidisciplinary Applications. A link to the conference's official proceedings will be provided upon publication.\n\nUpdate (2026-09-18): What Do We Expect from LLMs? Mapping the Design of LLM Benchmarks. arXiv:2609.19182v1 Announce Type: new \nAbstract: Benchmarks are central to how progress in large language models (LLMs) is assessed and communicated. Yet model rankings alone reveal little about how evaluation requirements themselves are changing. The expanding variety of benchmarks offers another perspective: what researchers expect LLMs to do, and what they count as successful performance. We systematically map 14,767 papers introducing or updating evaluation resources from arXiv submissions between January 2022 and August 2026. Using staged screening and automated full-text coding, we examine changes in target systems and domains, evaluation materials and conditions, and scoring mechanisms. The collection shows growing emphasis on action, interaction, and professional applications, while established and newer design elements frequently coexist. Model participation also develops unevenly: LLM-based scoring grows within both agent and non-agent groups, whereas model-generated materials show no comparable sustained increase in recent cohorts. These findings illuminate how public research trans\n\nUpdate (2026-09-22): Training a model to identify AI-generated web content from structure alone. Hey HN! We’re Vincent and Jochen from Sitefire (https://sitefire.ai). We have been working together for years, with backgrounds in RL/optimization at Stanford and software engineering from Technical University Munich (TUM).\nWith Sitefire (YC W26), we help marketing teams get recommended by AI Search (ChatGPT, Google AI Overviews, AI Mode, Claude, etc.). Our software monitors prompts, sees which web pages get cited, and uses these insights to help marketing teams take action, e.g. create YouTube videos or write the right blog posts.\nThis means we have a commercial stake in AI-generated web content. And for now, high-information, AI-generated content works great to get cited and recommended in AI Search.\nBut after talking to hundreds of marketing teams, it became clear that everyone despises AI-generated content (“AI slop”). And yet, everyone still wants to leverage AI to create content. So we asked ourselves: what characterizes AI slop? Can we train a model to identify it from human-generated web pages?\nResearchers from the University of Maryland and Google DeepMind already asked this \n\nUpdate (2026-09-24): STAT+: In radiology, AI is blurring the line between technology development and clinical practice. A new wave of radiology practices are aggressively embracing AI: developing and deploying it in-house, and marketing themselves as 'AI-native.'","ledger_type":"benefit","primary_domain_id":"fc7d617f-c9df-4b9f-b595-4408915ea380","event_status":"provisional","event_date":"2026-09-15T00:00:00.000Z","discovery_date":"2026-09-15T00:00:00.000Z","first_published_date":"2026-09-15T00:00:00.000Z","last_reviewed_date":"2026-09-24T00:00:00.000Z","geographic_scope":"International","affected_population":null,"base_impact_tier":1,"base_score":"1.00","attribution_multiplier":"0.1000","evidence_multiplier":"0.4000","realization_multiplier":"0.2000","durability_multiplier":"0.5000","current_event_score":"0.004000","confidence_level":"low","score_explanation":"Ingest update: independent source raised evidence; added a follow-up source. Score remains provisional until review. Follow-up ingest: independent source raised evidence; added a follow-up source. Follow-up ingest: independent source raised evidence; added a follow-up source.","methodology_version_id":"8745541b-0f8e-4d5a-9b47-5291d3813a8d","original_methodology_version_id":"8745541b-0f8e-4d5a-9b47-5291d3813a8d","published_at":"2026-09-15T04:00:56.143Z","created_at":"2026-09-15T04:00:56.143Z","updated_at":"2026-09-24T09:01:00.658Z","flags":[],"domain_name":"Other","domain_slug":"other","methodology_version":"0.2"},"contributions":[{"id":"5e565cad-626b-4254-95a9-10e7e0b0a555","event_id":"a67a83cd-ec23-4880-a76e-997b0104872b","model_id":"535014f7-c941-4d39-aa9b-8f9c1d3d07b7","role_description":"Unspecified system mentioned or implied by a news item. Remap to a named release when identified.","attribution_multiplier":"0.1000","credit_share":"1.0000","contribution_score":"0.004000","attribution_rationale":"News ingest does not infer a named model from the publisher alone. Attribution stays unspecified until a release is identified.","attribution_confidence":"medium","first_used_date":"2026-09-15T00:00:00.000Z","model_version_if_known":null,"review_status":"approved","created_at":"2026-09-15T04:00:56.154Z","model_slug":"unspecified-ai-system","model_name":"Unspecified AI system","identity_class":"unknown","is_internal":false,"family_name":"Unspecified","family_slug":"unknown-unspecified","organization_name":"Unknown","organization_slug":"unknown"}],"sources":[{"id":"e22ee65d-343a-468f-ae2a-26c546ab91d5","event_id":"a67a83cd-ec23-4880-a76e-997b0104872b","url":"https://arxiv.org/abs/2609.13535","canonical_url":"https://arxiv.org/abs/2609.13535","source_type":"preprint","publisher":"arXiv cs.AI","author":null,"publication_date":"2026-09-15T00:00:00.000Z","retrieved_at":"2026-09-15T04:00:56.161Z","title":"From Legal Text to AI-specific Risk Sources: A Systematic Analysis of the EU AI Act's High-Risk Requirements","excerpt":"arXiv:2609.13535v1 Announce Type: new \nAbstract: The EU AI Act introduces mandatory requirements for high-risk AI systems with the explicit goal of ensuring the development and operation of trustworthy AI. At the same time, AI risk management practices rely on structured risk taxonomies to systematically identify and treat AI-specific risk sources. As both the AI Act and established risk taxonomies aim to address AI-induced risks, a natural question is whether they align in the risk sources they","content_hash":null,"source_reliability_class":"medium","is_primary_source":true,"is_independent":true,"is_peer_reviewed":false,"archived_url":null,"created_at":"2026-09-15T04:00:56.161Z"},{"id":"f162436d-ab76-4ab8-9694-cee431ce8a93","event_id":"a67a83cd-ec23-4880-a76e-997b0104872b","url":"https://arxiv.org/abs/2609.19182","canonical_url":"https://arxiv.org/abs/2609.19182","source_type":"preprint","publisher":"arXiv cs.AI","author":null,"publication_date":"2026-09-18T00:00:00.000Z","retrieved_at":"2026-09-18T04:00:52.746Z","title":"What Do We Expect from LLMs? Mapping the Design of LLM Benchmarks","excerpt":"arXiv:2609.19182v1 Announce Type: new \nAbstract: Benchmarks are central to how progress in large language models (LLMs) is assessed and communicated. Yet model rankings alone reveal little about how evaluation requirements themselves are changing. The expanding variety of benchmarks offers another perspective: what researchers expect LLMs to do, and what they count as successful performance. We systematically map 14,767 papers introducing or updating evaluation resources from arXiv submissions b","content_hash":null,"source_reliability_class":"medium","is_primary_source":false,"is_independent":false,"is_peer_reviewed":false,"archived_url":null,"created_at":"2026-09-18T04:00:52.746Z"},{"id":"37393b8d-19fe-4c6a-9183-c5c18939c55d","event_id":"a67a83cd-ec23-4880-a76e-997b0104872b","url":"https://arxiv.org/abs/2609.15369","canonical_url":"https://arxiv.org/abs/2609.15369","source_type":"journalism","publisher":"Hacker News","author":null,"publication_date":"2026-09-22T00:00:00.000Z","retrieved_at":"2026-09-22T19:01:17.279Z","title":"Training a model to identify AI-generated web content from structure alone","excerpt":"Hey HN! We’re Vincent and Jochen from Sitefire (https://sitefire.ai). We have been working together for years, with backgrounds in RL/optimization at Stanford and software engineering from Technical University Munich (TUM).\nWith Sitefire (YC W26), we help marketing teams get recommended by AI Search (ChatGPT, Google AI Overviews, AI Mode, Claude, etc.). Our software monitors prompts, sees which web pages get cited, and uses these insights to help marketing teams take action, e.g. create YouTube ","content_hash":null,"source_reliability_class":"medium","is_primary_source":false,"is_independent":true,"is_peer_reviewed":false,"archived_url":null,"created_at":"2026-09-22T19:01:17.279Z"},{"id":"a89f937c-2039-4acf-be71-540c51000cd7","event_id":"a67a83cd-ec23-4880-a76e-997b0104872b","url":"https://www.statnews.com/2026/09/24/radiology-ai-blurred-line-between-tech-development-clinical-practice/?utm_campaign=rss","canonical_url":"https://www.statnews.com/2026/09/24/radiology-ai-blurred-line-between-tech-development-clinical-practice/?utm_campaign=rss","source_type":"journalism","publisher":"STAT","author":null,"publication_date":"2026-09-24T00:00:00.000Z","retrieved_at":"2026-09-24T09:01:00.642Z","title":"STAT+: In radiology, AI is blurring the line between technology development and clinical practice","excerpt":"A new wave of radiology practices are aggressively embracing AI: developing and deploying it in-house, and marketing themselves as 'AI-native.'","content_hash":null,"source_reliability_class":"medium","is_primary_source":false,"is_independent":true,"is_peer_reviewed":false,"archived_url":null,"created_at":"2026-09-24T09:01:00.642Z"}],"claims":[{"id":"cdecb0f2-d0b0-4404-809b-60ef9a38fbcd","event_id":"a67a83cd-ec23-4880-a76e-997b0104872b","claim_text":"From Legal Text to AI-specific Risk Sources: A Systematic Analysis of the EU AI Act's High-Risk Requirements","claim_type":"outcome","claim_status":"supported","confidence_score":null,"created_at":"2026-09-15T04:00:56.167Z","updated_at":"2026-09-15T04:00:56.167Z"}],"revisions":[{"id":"48c8080b-08a9-44a0-9401-7ed6cc6287e3","event_id":"a67a83cd-ec23-4880-a76e-997b0104872b","model_id":null,"previous_score":"0.000000","new_score":"0.001000","previous_factors":{},"new_factors":{"evidence":0.1,"base_score":1,"durability":0.5,"attribution":0.1,"realization":0.2},"change_reason":"Auto-published from news ingest.","trigger_type":"news_ingest","trigger_source_ids":null,"reviewer_id":null,"review_status":"published","created_at":"2026-09-15T04:00:56.175Z"},{"id":"16f9cd65-9fbc-4ff6-9fc8-2040375c4ea7","event_id":"a67a83cd-ec23-4880-a76e-997b0104872b","model_id":null,"previous_score":"0.001000","new_score":"0.002000","previous_factors":{"tier":1,"evidence":0.1,"base_score":1,"durability":0.5,"attribution":0.1,"realization":0.2},"new_factors":{"tier":1,"models":["535014f7-c941-4d39-aa9b-8f9c1d3d07b7"],"evidence":0.2,"base_score":1,"durability":0.5,"attribution":0.1,"realization":0.2},"change_reason":"Ingest update: independent source raised evidence; added a follow-up source.","trigger_type":"ingest_update","trigger_source_ids":["f162436d-ab76-4ab8-9694-cee431ce8a93"],"reviewer_id":null,"review_status":"published","created_at":"2026-09-18T04:00:52.780Z"},{"id":"e20e0447-b468-435d-bd9e-36c2ba0704a1","event_id":"a67a83cd-ec23-4880-a76e-997b0104872b","model_id":null,"previous_score":"0.002000","new_score":"0.003000","previous_factors":{"tier":1,"evidence":0.2,"base_score":1,"durability":0.5,"attribution":0.1,"realization":0.2},"new_factors":{"tier":1,"models":["535014f7-c941-4d39-aa9b-8f9c1d3d07b7"],"evidence":0.3,"base_score":1,"durability":0.5,"attribution":0.1,"realization":0.2},"change_reason":"Ingest update: independent source raised evidence; added a follow-up source.","trigger_type":"ingest_update","trigger_source_ids":["37393b8d-19fe-4c6a-9183-c5c18939c55d"],"reviewer_id":null,"review_status":"published","created_at":"2026-09-22T19:01:17.304Z"},{"id":"a6ed3bcb-9ba9-4402-93c6-1c2e49b4a528","event_id":"a67a83cd-ec23-4880-a76e-997b0104872b","model_id":null,"previous_score":"0.003000","new_score":"0.004000","previous_factors":{"tier":1,"evidence":0.3,"base_score":1,"durability":0.5,"attribution":0.1,"realization":0.2},"new_factors":{"tier":1,"models":["535014f7-c941-4d39-aa9b-8f9c1d3d07b7"],"evidence":0.4,"base_score":1,"durability":0.5,"attribution":0.1,"realization":0.2},"change_reason":"Ingest update: independent source raised evidence; added a follow-up source.","trigger_type":"ingest_update","trigger_source_ids":["a89f937c-2039-4acf-be71-540c51000cd7"],"reviewer_id":null,"review_status":"published","created_at":"2026-09-24T09:01:00.673Z"}],"secondary":[]},"methodology_version":"0.1"}