{"id":64482,"date":"2025-07-08T20:15:06","date_gmt":"2025-07-08T20:15:06","guid":{"rendered":"https:\/\/pros.com\/b2b\/\/?post_type=news&#038;p=64482"},"modified":"2025-07-14T20:17:31","modified_gmt":"2025-07-14T20:17:31","slug":"the-layered-ai-responsibility-lair-framework","status":"publish","type":"news","link":"https:\/\/pros.com\/b2b\/de\/news\/the-layered-ai-responsibility-lair-framework\/","title":{"rendered":"Der LAIR-Rahmen (Layered AI Responsibility)"},"content":{"rendered":"<p>Da gro\u00dfe Sprachmodelle (LLMs) immer intelligenter werden und KI-Agenten immer mehr Werkzeuge und F\u00e4higkeiten erhalten, wird die verantwortungsvolle Entwicklung dieser Technologien immer wichtiger. Da ich sowohl in der Industrie als auch im akademischen Bereich viel gearbeitet habe, habe ich eine gro\u00dfe Kluft bei der Priorisierung von verantwortungsvoller KI beobachtet. <\/p>\n<h2>Einleitung: Die Perspektiven der Wissenschaft und der Industrie auf verantwortungsvolle KI<\/h2>\n<p>Die akademische Welt neigt dazu, verantwortungsvolle KI mit einer idealistischen Einstellung anzugehen. Studenten und Forschern wird beigebracht, vom ersten Tag an und w\u00e4hrend des gesamten Entwicklungsprozesses ethische, rechtliche und sicherheitstechnische \u00dcberlegungen in KI-Systeme einzubauen. Dieser Ansatz ist wertvoll und gew\u00e4hrleistet, dass der h\u00f6chste Standard erreicht wird. In der Industrie, wo finanzieller Druck und Produktfristen eine schnelle Entwicklung und Einf\u00fchrung erfordern, ist er jedoch oft nicht praktikabel.<\/p>\n<p>&nbsp;<\/p>\n<p><a class=\"pill-button\" href=\"https:\/\/aijourn.com\/the-layered-ai-responsibility-lair-framework\/\" target=\"_blank\" rel=\"noopener\">Mehr lesen<\/a><\/p>","protected":false},"excerpt":{"rendered":"<p>As large language models (LLMs) grow smarter and AI agents gain more tools and capabilities, the responsible development of these technologies becomes paramount. Having worked extensively in both industry and academia, I have observed a significant divide in how responsible AI is prioritized. Introduction: Academia vs. Industry Perspectives on Responsible AI Academia tends to approach [&hellip;]<\/p>\n","protected":false},"featured_media":0,"template":"","meta":{"_acf_changed":false,"inline_featured_image":false},"news-type":[20],"class_list":["post-64482","news","type-news","status-publish","hentry","news-type-pros-in-the-news"],"acf":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO Premium plugin v26.7 (Yoast SEO v27.3) - https:\/\/yoast.com\/product\/yoast-seo-premium-wordpress\/ -->\n<title>The Layered AI Responsibility (LAIR) Framework | PROS B2B<\/title>\n<meta name=\"robots\" content=\"noindex, nofollow\" \/>\n<meta property=\"og:locale\" content=\"de_DE\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"The Layered AI Responsibility (LAIR) Framework\" \/>\n<meta property=\"og:description\" content=\"As large language models (LLMs) grow smarter and AI agents gain more tools and capabilities, the responsible development of these technologies becomes paramount. Having worked extensively in both industry and academia, I have observed a significant divide in how responsible AI is prioritized. 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