Why did Senator Jerry Moran create AI risk management bills?

Senator Jerry Moran, a Republican from Kansas, has been serving in the U.S. Senate since 2011. Born on May 29, 1954, in Great Bend, Kansas, he was raised in Plainville. He earned a Bachelor of Science degree in economics from the University of Kansas in 1976 and later obtained a Juris Doctor from the University of Kansas School of Law. Before his tenure in the Senate, Moran served in the Kansas Senate from 1989 to 1997, acting as majority leader during his final two years. Subsequently, he represented Kansas’s 1st congressional district in the U.S. House of Representatives from 1997 to 2011.

Throughout his political career, Senator Moran has been a staunch advocate for rural Kansans, focusing on issues related to agriculture, veterans’ affairs, and economic development. He has held positions on several key Senate committees, including Appropriations; Commerce, Science, and Transportation; and Veterans’ Affairs. In the 119th Congress, which convened on January 3, 2025, Senator Moran announced his committee assignments, continuing his commitment to serving the interests of Kansans.

In recent legislative efforts, Senator Moran has been involved in initiatives to reform Haskell Indian Nations University. Alongside Representative Tracey Mann, he announced a discussion draft of legislation aimed at federally chartering the university, thereby removing it from the Bureau of Indian Education’s oversight. This move is intended to provide the university with greater autonomy and improve its governance structure.

Senator Moran’s dedication to his constituents and his active role in legislative matters continue to make him a prominent figure in Kansas politics and on the national stage.

Senator Jerry Moran introduced AI risk management legislation to address the growing importance and complexity of artificial intelligence (AI) within federal operations and its potential risks. As AI technology continues to advance and become integrated into various aspects of government, business, and daily life, the need for a standardized framework to manage its deployment and use has become increasingly urgent. Senator Moran, in collaboration with Senator Mark Warner, put forward the Federal Artificial Intelligence Risk Management Act to establish a consistent approach for federal agencies in handling AI systems.

The core of this legislation focuses on requiring federal agencies to adopt the AI Risk Management Framework developed by the National Institute of Standards and Technology (NIST). This framework provides guidelines to help organizations understand and mitigate risks associated with AI technologies. By mandating its implementation, the bill aims to ensure that AI systems are used responsibly, with an emphasis on security, reliability, and ethical considerations. Senator Moran sees this as a way to harness the benefits of AI while proactively addressing the potential downsides, such as vulnerabilities in data security, biases in algorithms, and the spread of misinformation.

One of the driving motivations behind this initiative is the dual challenge of encouraging innovation while safeguarding public and national interests. AI has the potential to enhance government efficiency, streamline operations, and improve services for citizens. However, it also introduces risks that, if left unchecked, could lead to unintended consequences. Senator Moran’s approach reflects a commitment to striking a balance between fostering technological progress and implementing necessary safeguards.

The bill underscores the importance of consistency in how federal agencies use AI. Without clear and uniform guidelines, the deployment of AI systems across different departments and agencies could lead to disparities in effectiveness, security, and accountability. By adhering to the NIST framework, federal agencies would have a unified standard for evaluating and managing AI risks, ensuring that these systems align with best practices and public expectations.

Senator Moran has emphasized the importance of addressing AI risks proactively rather than reactively. He recognizes that while AI offers significant advantages, it also carries ethical and operational risks that must be addressed at the outset. By proposing this legislation, Moran aims to position the federal government as a leader in responsible AI adoption, setting an example for other organizations and sectors.

The introduction of this bill also reflects broader concerns about AI’s role in shaping society and its potential to impact areas such as privacy, national security, and public trust. As AI becomes more pervasive, the need for robust governance structures and risk management strategies becomes critical. Senator Moran’s efforts to establish these frameworks highlight the importance of preparing for AI’s challenges while embracing its opportunities. Through this legislation, Moran seeks to create a foundation for the responsible and secure use of AI within government operations, ensuring that technological advancements benefit society as a whole.

Implementing AI risk management frameworks, as proposed by Senator Moran, comes with several problems and challenges. These issues stem from the complexity of AI systems, the rapid pace of technological advancement, and the need to balance innovation with regulation.

One major challenge is the lack of standardized understanding and definitions of AI risks across different federal agencies. AI technologies vary widely in their design and application, ranging from simple algorithms to complex machine learning systems. This diversity makes it difficult to develop a one-size-fits-all framework that addresses the unique risks of each system. Agencies may struggle to adapt the National Institute of Standards and Technology (NIST) framework to their specific needs while maintaining consistency and interoperability.

The pace of AI innovation also complicates risk management. AI technologies evolve rapidly, and frameworks developed today may become obsolete as new capabilities and challenges emerge. Ensuring that the framework remains relevant requires continuous updates, collaboration with AI developers, and investments in research. However, government processes can be slower than the speed of technological change, potentially leaving agencies vulnerable to outdated practices.

Another issue is the resource and expertise gap within federal agencies. Effective implementation of an AI risk management framework requires technical expertise in AI and its risks, as well as sufficient resources to monitor and manage these systems. Many agencies may lack the trained personnel, budget, or infrastructure necessary to fully comply with the requirements of the proposed legislation. Smaller agencies, in particular, might face significant barriers in aligning with the framework.

There are also privacy and data protection concerns associated with AI risk management. To monitor and mitigate risks, agencies may need to collect and analyze large datasets, which could inadvertently expose sensitive information. Striking a balance between effective oversight and the protection of individual privacy is a delicate and challenging task.

The global nature of AI development adds another layer of complexity. AI technologies are often created by international companies or involve data and algorithms sourced from multiple countries. Coordinating risk management efforts across borders and addressing potential conflicts with international regulations can be a significant hurdle. For example, differing standards between the United States and other regions, such as the European Union’s General Data Protection Regulation (GDPR), might lead to inconsistencies or conflicts.

There is also the risk of stifling innovation if the framework is perceived as overly restrictive. Companies and developers may be deterred from engaging with federal agencies or might avoid implementing cutting-edge technologies due to fears of non-compliance. This could limit the government’s ability to leverage the full potential of AI while also reducing the incentives for private-sector collaboration.

Lastly, the ethical dimensions of AI governance present challenges. Questions about accountability, bias, and the societal impacts of AI systems are difficult to address within a technical framework alone. Agencies need to navigate these ethical concerns while ensuring transparency and public trust in the systems they deploy. The complexity of these issues may lead to delays in implementation or disagreements about the appropriate approach.

Overall, while Senator Moran’s proposal to implement AI risk management frameworks is a forward-thinking step, it requires addressing these significant challenges to ensure successful adoption and meaningful results. Navigating these issues will demand collaboration across agencies, investment in expertise, and a commitment to balancing technological advancement with ethical and practical safeguards.