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Across the Field

Policymakers and advocates need stronger methods to challenge automated decisions.

We seek to lay a strong foundation to challenge automated decisions across our issues. We look for structural opportunities to improve how governments and companies measure racial disparities, and to support advocates by expanding both legal protections and access to the information necessary to produce independent analyses. This work also includes detailed investigations into specific platforms, such as Facebook’s role in driving discrimination using targeted online advertising.

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The Fair Lending Model

Emily Black, Miranda Bogen, Logan Koepke, Solon Barocas, Wesley Deng, Mingwei Hsu

Our paper with collaborators at New York University, the Center for Democracy & Technology, Microsoft Research, and Carnegie Mellon University, presented at the 2026 ACM Conference on Fairness, Accountability, and Transparency that offers one of the first empirical accounts of how financial institutions test for and mitigate algorithmic discrimination on the ground. In doing so, we also shed light on how the regulatory design of fair lending law and regulation has shaped the policies, processes, and practices of fair lending teams.

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Latest work in this issue area

All work in this issue area
Explainer: Addressing Discrimination in Social Media Ad Delivery

This explainer is derived from a lengthier analysis written by Princeton and USC researchers of Meta’s recent efforts to mitigate discrimination in its advertisement delivery.

Mitra Ebadolahi, with feedback from Aleksandra Korolova and Basileal Imana

Letter to DOJ re: AI Executive Order

Specific recommendations on implementing the recently-signed Executive Order on Safe, Secure, and Trustworthy Development and Use of Artificial Intelligence (EO 14110).

Upturn, Center for Democracy & Technology, Lawyers’ Committee for Civil Rights Under Law, & seven groups

Comments on OMB's AI Memorandum

We wrote comments in response to the Office of Management and Budget’s draft memorandum, Advancing Governance, Innovation, and Risk Management for Agency Use of Artificial Intelligence (AI).

Logan Koepke and Harlan Yu

Less Discriminatory Algorithms

Our paper on how entities that use algorithmic systems in traditional civil rights domains like housing, employment, and credit should have a duty to search for and implement less discriminatory algorithms (LDAs).

Emily Black, Logan Koepke, Pauline Kim, Solon Barocas, and Mingwei Hsu

Selected press and events

WIRED Facebook’s Ad System Might Be Hard-Coded for Discrimination

WIRED covers Upturn’s research on how Facebook’s ad delivery system may perpetuate bias.

The Verge Facebook’s ad delivery could be inherently discriminatory, researchers say

Coverage from The Verge on Upturn’s research into how Facebook’s ad system can skew delivery outcomes.

Economist Facebook’s ad system seems to discriminate by race and gender

The Economist covers Upturn’s research on Facebook’s seemingly discriminatory ad system.

Reuters Facebook's ads system leans on stereotypes for housing, job ads: study

“Facebook’s algorithms, which match marketing messages with viewers, leans on stereotypes when it comes to housing and jobs, according to [Upturn’s empirical work].”