RegLab partnered with the states of New York, California, and Maryland to help these governments start paring these provisions from their books to speed the pace of government. Reflecting on this partnership with RegLab, Maryland’s Secretar…
Arnold Ventures recently featured RegLab’s collaboration with the Colorado Department of Labor and Employment to build and rigorously evaluate an AI tool to assist with adjudication of unemployment claims. The pilot, which recently earned a…
RegLab’s 2026 Summer Institute is in full swing! This year’s cohort includes PhD students Betty Hou, Stephanie Wang, and Rachel Kim; master’s students Nancy Jiang, Owen Jiang, Javokhir Arifov, JD student Emaan Hariri; and undergraduates Wil…
California is home to the leading technologists rapidly pushing the development of artificial intelligence. While technology almost always moves at a faster pace than legislation, policymakers still play a key role in navigating how AI will…
October 30, 2024 | Ben Bain and Christine Tsang The United States needs adaptable and responsive government institutions to effectively tackle the complex challenges facing the country both domestically and internationally. One major obstac…
October 17, 2024 | RegLab and the County of Santa Clara When Dan Ho purchased a home in Palo Alto, he recounts, “We had to sign papers that said that the ‘property shall not be used or occupied by any person of African, Japanese or Chinese
We are thrilled to welcome Elena Eneva as a new Research Director at RegLab. With over 20 years of expertise in artificial intelligence, machine learning, and data science, Elena brings a wealth of experience across industry, academia, and
Daniel E. Ho | Olivia Martin | Amy Perez | Kit RodolfaAdministrative Law Review, March 2025 Modern government is built on the civil service. The twentieth century consensus, emerging out of the New Deal, was based on a social safety net adm…
Emily Black | Hadi Elzayn | Alexandra Chouldechova | Jacob Goldin | Daniel E. HoACM FAccT 1479, June 2022 This study examines issues of algorithmic fairness in the context of systems that inform tax audit selection by the United States Inte…
Arushi Gupta | Victor Y. Wu | Helen Webley-Brown | Jennifer King | Daniel E. HoFAccT, June 2023 An emerging concern in algorithmic fairness is the tension with privacy interests. Data minimization can restrict access to protected attributes…
Concentrated Animal Feeding Operations (CAFOs) house livestock at high densities for prolonged periods of time, posing substantial risks to environmental and human health. However, limited public information on CAFOs has constrained efforts…
A key task in legal writing is identifying the legal authority governing a particular set of facts. While a flurry of research and especially private-sector tools claim to use machine learning to assist lawyers in quickly finding the proper…