{"id":22752,"date":"2021-04-20T07:58:00","date_gmt":"2021-04-20T07:58:00","guid":{"rendered":"https:\/\/www.experfy.com\/blog\/ai-leaders-prepare-the-looming-eu-regulations\/"},"modified":"2023-08-26T05:39:33","modified_gmt":"2023-08-26T05:39:33","slug":"ai-leaders-prepare-the-looming-eu-regulations","status":"publish","type":"post","link":"https:\/\/www.experfy.com\/blog\/ai-ml\/ai-leaders-prepare-the-looming-eu-regulations\/","title":{"rendered":"How AI Leaders Should Prepare For The Looming EU Regulations"},"content":{"rendered":"\t\t
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New regulations impose obligations, including post-market monitoring, on all companies using \u201chigh-risk\u201d AI in the EU<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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Last week, a draft of the EU\u2019s highly anticipated Regulation on A European Approach For Artificial Intelligence<\/a> was leaked. The official version is expected this week.<\/p>\n

Although as non-lawyers, we cannot offer a legal analysis of the regulation, we can say that the draft makes clear that the EU is preparing to take a robust approach to regulating AI, from ensuring good data sets, to proper testing and training of the system, required registration in a newly created EU database, mandatory post-market monitoring, and penalties for those who fail to comply.<\/p>\n

The regulation has broad implications. Although most of the provisions are limited to \u201chigh risk\u201d AI systems, \u201chigh risk\u201d seems to be defined quite broadly. The draft directly lists some \u201chigh risk\u201d AI use-cases such as employee \/ candidate assessment, determining creditworthiness and operating essential public infrastructure. Additionally, the draft declares that the parameters defining \u201chigh risk AI\u201d would be dynamic, and that the commission is empowered to include additional use-cases under these definitions according to the severity and probability of causing harm.<\/p>\n

Time will tell just how broadly this regulation is applied and how many different types of AI systems are covered. However, as the term \u201chigh risk\u201d is broadly defined, and the regulation encourages providers of non-high-risk AI to comply as well, forward-thinking companies should start planning now how they will comply with this new, sweeping regulation.<\/p>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t

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So, what should you do to prepare?<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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1. Time to change your thinking<\/h3>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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Throughout the draft, \u201cAI systems\u201d and not \u201cmodels\u201d is the prevailing term. This shift from \u201cmodel\u201d to \u201csystem\u201d may be challenging for data scientists as traditionally, the \u201cmodel\u201d has been the end goal of the research project. Additionally, many of the tools used by data scientists are model centric. That might work for the research phase, but production systems typically contain more than the one model (and many other pieces besides the model), and it\u2019s the entire AI system that will need to be registered (yes, all high-risk AI systems will be registered in an EU database). It\u2019s the entire AI system that will need to pass conformity tests.<\/p>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t

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2. Get organized!<\/h3>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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Photo by Jonah Pettrich<\/a> on Unsplash<\/a><\/figcaption>\n\t\t\t\t\t\t\t\t\t\t<\/figure>\n\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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The draft makes clear that your AI system must be organized, transparent, and documented. Specifically, you must:<\/p>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t

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