Algorithmic Fairness

Publication

Fairness through Difference Awareness: Measuring Desired Group Discrimination in LLMs

ArXiV
August 2025
Post

The California Report on Frontier AI Policy

Dan Ho and other members of the RegLab team contributed to the report produced by the Joint California Policy Working Group on AI Frontier Models, which offers a policy framework for responsible, ethical, and safe use of AI.
Post

AI’s Fairness Problem: When Treating Everyone the Same is the Wrong Approach

Current generative AI models struggle to recognize when demographic distinctions matter—leading to inaccurate, misleading, and sometimes harmful outcomes.
Post

Considerations for governing open foundation models

Dan Ho co-authored a new article published in Science that analyzes the impact of different policy proposals related to regulating open foundation models.
Publication

On the Societal Impact of Open Foundation Models

February 2024
Publication

Estimating and Implementing Conventional Fairness Metrics With Probabilistic Protected Features

Conference on Secure and Trustworthy Machine Learning
October 2023
Publication

How Redundant are Redundant Encodings? Blindness in the Wild and Racial Disparity when Race is Unobserved

ACM FAccT
June 2023
Post

IRS confirms RegLab study of racial bias in tax audits

Publication

Measuring and Mitigating Racial Disparities in Tax Audits

Working Paper
January 2023
Publication

Algorithmic Fairness and Vertical Equity: Income Fairness with IRS Tax Audit Models

ACM FAccT 1479
June 2022
Publication

On the Opportunities and Risks of Foundation Models

arXiv
August 2021
Publication

How medical AI devices are evaluated: limitations and recommendations from an analysis of FDA approvals

Nature Medicine
April 2021
Publication

The Distributive Effects of Risk Prediction in Environmental Compliance: Algorithmic Design, Environmental Justice, and Public Policy

ACM FAccT 2021
March 2021
Publication

How US Law Will Evaluate Artificial Intelligence for Covid-19

BMJ
March 2021
Publication

Leveraging Administrative Data for Bias Audits: Assessing Disparate Coverage with Mobility Data for COVID-19 Policy

ACM FAccT 2021
March 2021
Publication

Evaluating Facial Recognition Technology: A Protocol for Performance Assessment in New Domains

Stanford's Institute for Human-centered Artificial Intelligence White Paper
November 2020
Publication

Affirmative Algorithms: The Legal Grounds for Fairness as Awareness

U. Chicago Law Review Online
October 2020
Publication

AI’s Promise and Peril for the U.S. Government

HAI Policy Briefs
September 2020
Publication

Algorithmic Accountability in the Administrative State

Yale Journal on Regulation
August 2020
Publication

What To Do About Artificially Intelligent Government

The Hill
February 2020
Publication

Government by Algorithm: Artificial Intelligence in Federal Administrative Agencies

Report to the Administrative Conference of the United States
February 2020
Publication

When Algorithms Import Private Bias into Public Enforcement: The Promise and Limitations of Statistical Debiasing Solutions

Journal of Institutional and Theoretical Economics
January 2019
Publication

Equity in the Bureaucracy

UC Irvine Law Review
June 2019
Project

Algorithmic Fairness

Ensuring fairness and transparency in AI

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