Discover how RegLab students from Stanford Law School have made a significant impact with their award-winning scholarships on artificial intelligence and public policy. Two research papers co-authored by these students received “best paper” awards at conferences focused on AI, trustworthy technology, and public policy. The papers delve into critical areas for the federal government to consider when it comes to the regulation and implementation of AI. Not only have these papers sparked attention and calls for change within the government, but they have also inspired responses from members of Congress, the White House, and federal agencies. Through their research, these students are shedding light on the challenges and potential solutions surrounding AI governance and equity assessments. Their findings emphasize the need for increased resources, leadership, and transparency in order to effectively adopt and regulate AI.
RegLab Students Make an Impact with Award-Winning Scholarship
The Regulation, Evaluation, and Governance Lab (RegLab) at Stanford Law School is celebrating the recognition of two research papers co-authored by its students in the field of artificial intelligence (AI) and public policy. These papers have received “best paper” awards at prestigious conferences held in the summer of 2023. The awards highlight RegLab’s contribution to AI scholarship and its impact on government policy recommendations.
Recognition for Research Papers on AI and Public Policy
The two papers, written by Stanford Law School students, have garnered significant attention and acclaim in the field of AI and public policy. These papers were recognized for their insightful analysis and critical exploration of key areas related to AI ethics, governance, and its implications for government and public discourse. The awards they received at the conferences reflect the importance and relevance of their research in shaping the future of AI regulation and policymaking.
Two Papers Receive “Best Paper” Awards
At the Sixth AAAI/ACM Conference on AI Ethics and Society (AIES) held in Montreal, Canada, Christie Lawrence, JD ’24, and Isaac Cui, JD ’25, were honored with a “best paper” award for their research on “The Bureaucratic Challenge to AI Governance: An Empirical Assessment of Implementation at U.S. Federal Agencies.” This paper delves into the complexities of AI implementation in federal agencies, shedding light on the challenges and opportunities for effective governance.
Another award-winning paper, titled “The Privacy-Bias Tradeoff: Data Minimization and Racial Disparity Assessments in U.S. Government,” was recognized at the Conference on Fairness, Accountability, and Transparency (FAccT). The authors, Victor Wu, Arushi Gupta, and Helen Webley Brown, explored the delicate balance between data collection and privacy in government programs, emphasizing the need to collect demographic data for equity assessments while safeguarding individual privacy.
Highlighting RegLab’s Contribution to AI Scholarship
The awards received by these research papers underscore the significant contribution of RegLab to the field of AI scholarship. RegLab, an interdisciplinary impact lab at Stanford Law School, collaborates with government agencies to modernize governance through the application of data science and machine learning. The lab’s partnership with these agencies allows for the advancement of innovation and the adoption of cutting-edge technologies in government processes.
The Importance of AI in Law and Policy
The role of AI in shaping government and public policy cannot be underestimated. AI has enormous potential to revolutionize various fields and bring about significant advancements. However, its regulation poses both challenges and opportunities. National AI policy plays a crucial role in guiding and regulating AI applications, ensuring their ethical use, and addressing potential risks. It is essential for policymakers and researchers to engage in robust debates on AI regulation to promote responsible and effective governance.
RegLab’s Role in Modernizing Government with Data Science and Machine Learning
RegLab stands at the forefront of efforts to modernize government through the application of data science and machine learning. By leveraging these cutting-edge technologies, RegLab aims to transform government processes, making them more efficient and effective. The lab’s interdisciplinary approach and partnerships with government agencies enable the development and implementation of innovative solutions that address contemporary governance challenges.
Winning Paper: The Bureaucratic Challenge to AI Governance
The paper titled “The Bureaucratic Challenge to AI Governance: An Empirical Assessment of Implementation at U.S. Federal Agencies,” authored by Christie Lawrence and Isaac Cui, received recognition at the AAAI/ACM Conference on AI Ethics and Society. This comprehensive empirical study assesses the implementation of AI in U.S. federal agencies, shedding light on the challenges faced in governing AI systems. The findings of this research call for changes in government AI regulation and highlight the need for senior-level leadership and capacity building within agencies to effectively address AI governance issues.
Impact of the Paper on Government Capacity Building
The research presented in “The Bureaucratic Challenge to AI Governance” has had a significant impact on government capacity building. The paper’s insights into the inconsistencies in AI transparency measures have prompted serious attention and generated calls for changes in how the government regulates AI. The study also highlights the importance of senior-level leadership and addresses capacity constraints within agencies. The implications of this research will inform future AI policies and contribute to more effective governance of AI technologies.
Winning Paper: The Privacy-Bias Tradeoff
Another award-winning paper, “The Privacy-Bias Tradeoff: Data Minimization and Racial Disparity Assessments in U.S. Government,” authored by Victor Wu, Arushi Gupta, and Helen Webley Brown, received recognition at the Conference on Fairness, Accountability, and Transparency. This paper explores the delicate tradeoff between data collection for equity assessments and individual privacy in government programs. It discusses the legal and bureaucratic barriers that hinder comprehensive demographic data collection and proposes solutions to strike a balance between privacy and addressing systemic biases.
Importance of Collecting Demographic Data for Equity Assessments
Collecting demographic data is essential for identifying and rectifying institutional biases in government programs. However, privacy concerns and legal barriers have limited the collection of such data for several decades. The paper on the privacy-bias tradeoff emphasizes the need to collect demographic data to conduct equity impact assessments while respecting individual privacy. The authors propose innovative solutions that interpret existing privacy laws to allow for inter-agency record linkage for bias assessment, ensuring equitable delivery of public services.
Solutions for Balancing Data Collection and Privacy
To strike a balance between data collection and privacy, the authors propose several solutions. Interpreting privacy laws, like the Privacy Act, to permit inter-agency record linkage for bias assessment can preserve privacy protections while enabling comprehensive equity assessments. Furthermore, maintaining a firewall between the unit conducting equity assessments and the unit administering the program ensures the equitable delivery of public services without compromising individual privacy. These solutions provide a framework for addressing systemic biases while respecting privacy.
In conclusion, the RegLab students’ award-winning scholarship on AI and public policy is a testament to their expertise and the impactful research conducted at Stanford Law School. Their papers highlight critical areas for consideration in AI governance and the importance of data collection and privacy in equitable assessments. RegLab’s role in modernizing government and its interdisciplinary approach further emphasizes the lab’s commitment to innovation and improving governance through data science and machine learning. The recognition received by these research papers paves the way for informed and comprehensive AI regulation and policymaking, ensuring responsible and ethical use of AI technology in the future.
