AI for Governance
Innovation at the Library of Congress with Natalie Buda Smith
On January 23, the Rebooting Democracy in the Age of AI lecture series featured Natalie Buda Smith, Director of Digital Strategy at the Library of Congress, discussing how Congress can leverage AI to enhance operations. Her team is experimenting with generative AI for legislative data analysis, developing AI-generated bill summaries, testing commercial and open-source models for legislative content, and enhancing the Congress.gov API. AI-powered tools also help monitor system performance. While still in early stages, these initiatives aim to support staff workflows rather than replace human expertise, prioritizing authenticity and accuracy in Congressional work. Watch the full recording here.
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AI Prototypes for UK Welfare System Dropped as Officials Lament ‘False Starts'
“Pilots of AI technology to enhance staff training, improve the service in job centres, speed up disability benefit payments and modernise communication systems are not being taken forward, freedom of information (FoI) requests reveal. Officials have internally admitted that ensuring AI systems are “scalable, reliable [and] thoroughly tested” are key challenges and say there have been many “frustrations and false starts”.
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‘Serious concerns’ about DWP’s use of AI to read correspondence from benefit claimants
The UK’s Department for Work and Pensions (DWP) is facing criticism for its use of AI, known as “white mail,” to process the 25,000 letters and emails it receives daily, aiming to prioritize vulnerable claimants more efficiently. Previously, human review took weeks, but the AI completes the task in a day. However, concerns have been raised about transparency and data privacy, as claimants are not informed of its use, despite the AI handling sensitive personal information like medical and financial details.
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GOVERNMENT AGENTS
In his essay, Harvard Business School professor and former City of Boston Chief of Staff Mitchell Weiss explores how AI agents can transform government operations by making public sector workflows more efficient. Rather than replacing human workers, these AI agents—each designed for specific tasks—could streamline processes like data collection, prototyping, and user testing, allowing government employees to focus on strategic decision-making. Weiss envisions a future where teams of AI "agents" collaborate to enhance government functions, from processing applications to improving service delivery. By integrating AI tools thoughtfully, he argues, governments can significantly boost productivity while maintaining human oversight and judgment. For more on AI Agents in Government, see last month’s essay by Tiago Peixoto of the World Bank.
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Introducing ChatGPT Gov
OpenAI has announced a new product, ChatGPT Gov, which the company describes as “a new tailored version of ChatGPT designed to provide U.S. government agencies with an additional way to access OpenAI’s frontier models. Rather than introducing new functionalities, ChatGPT Gov focuses on enhanced security measures, such as allowing agencies to host the tool within their secure hosting environments The product launch, which some see as a response to the big splash made by DeepSeek’s launch last week, signals OpenAI’s strategic push to promote the use of its tools by U.S. government. OpenAI also wondered out loud (Mashable) whether DeepSeek has stolen data from them, which many greeted as ironic in light of lawsuits against OpenAI accusing it of ripping off copyrighted data.
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Measuring and Mitigating Racial Disparities in Tax Audits
The study examines how audit selection algorithms contribute to racial disparities in IRS tax audits, particularly for Black taxpayers claiming the Earned Income Tax Credit (EITC). Abstract: “Tax authorities around the world rely on audits to detect underreported tax liabilities and to verify that taxpayers qualify for the benefits they claim. We study differences in Internal Revenue Service audit rates between Black and non-Black taxpayers. Because neither we nor the IRS observe taxpayer race, we propose and use a novel partial identification strategy to estimate these differences. Despite race-blind audit selection, we find that Black taxpayers are audited at 2.9 to 4.7 times the rate of non-Black taxpayers. An important driver of the disparity is differing audit rates by race among taxpayers claiming the Earned Income Tax Credit (EITC).”
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