Key regulatory frameworks
1. Key regulatory frameworks
Welcome. In this video, we’ll examine the external forces shaping AI governance, summarizing key global regulations like the EU AI Act, the U.S. Executive Order, and China’s algorithm rules. We’ll also look at how they assign obligations based on risk and mandate core requirements like documentation and oversight. Jordan: Hi, Simla. In our last session, we talked about the components of AI governance. Now, I'm curious about the external factors. Are there specific laws or regulations shaping how organizations need to govern AI? Simla: Absolutely, Jordan. This is a rapidly evolving area, and several key regulatory frameworks are already emerging worldwide that significantly influence AI governance practices. Jordan: Such as? Can you give me some examples? Simla: Certainly. One of the most prominent is the European Union's AI Act. This is a comprehensive piece of legislation that takes a risk-based approach to regulating AI. Jordan: A risk-based approach? What does that mean? Simla: It means that the obligations and requirements placed on AI systems depend on the level of risk they are deemed to pose. Systems considered to have an unacceptable risk are prohibited, while high-risk systems, such as those used in critical infrastructure or employment decisions, are subject to strict governance requirements. Jordan: That sounds logical – more scrutiny for more potentially harmful AI. What about other regions? Simla: In the U.S., AI governance is guided by the Executive Order on Safe, Secure, and Trustworthy AI, which sets a broad federal strategy and directs agencies to create specific guidelines. China has issued rules on algorithmic recommendations to promote fairness and protect consumers. Internationally, frameworks like the Council of Europe's AI Convention are emerging, alongside regional measures such as a regional law in Ontario, Canada, requiring employers to disclose AI use in hiring. Jordan: So, it's a multi-layered landscape with international, national, and even regional regulations emerging. It sounds like organizations need to stay very informed. Simla: Precisely. And while they vary, a common thread is how they assign governance obligations based on risk. For high-risk systems, regulations demand much more. Jordan: So, what governance actions are typically required by law for these high-risk systems? Simla: For high-risk AI, you'll commonly see requirements for risk classification itself – formally documenting why it's high-risk. Then there are conformity assessments, which are evaluations to prove the system meets regulatory standards before it's used. Jordan: So, is it moving towards a "document, register if needed, and ensure human involvement" approach for certain AI rather than just "build it and deploy it"? Simla: Exactly. These regulations aim for greater accountability and transparency throughout the AI lifecycle, recognizing the need to manage risks and ensure responsible use as AI becomes more integrated into our lives. Jordan: This is really helpful in understanding the external pressures shaping AI governance. Organizations must know these different frameworks and tailor their governance practices accordingly. Simla: Absolutely. Navigating this evolving regulatory landscape is becoming a key aspect of responsible AI development and deployment.2. Let's practice!
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