Algorithmic Justice League

The Algorithmic Justice League (AJL) is a Cambridge, Massachusetts-based nonprofit that combines art, research, and advocacy to surface the harms of biased and unaccountable AI and to build the public capacity to push back. The organization was founded in 2016 by Joy Buolamwini, then a graduate student at the MIT Media Lab, after a now-widely-cited experiment in which off-the-shelf facial-detection software failed to register her face until she put on a white mask. AJL's stated mission is to raise public awareness about the impacts of AI, equip advocates with resources, build the voice and choice of the most-affected communities, and galvanize researchers, policymakers, and industry practitioners to prevent AI harms.

Programs and major work

AJL's work spans research, public-facing storytelling, community-reporting infrastructure, and creators'-rights advocacy. The signature early project was Gender Shades, a 2018 study by Buolamwini and Timnit Gebru that audited commercial facial-analysis systems from IBM, Microsoft, and Megvii and found dramatic accuracy gaps for darker-skinned and feminine-presenting faces. The findings prompted product changes by the audited vendors and seeded years of subsequent regulatory and corporate response. AJL has since expanded its remit beyond facial analysis to algorithmic decision-making, algorithmic governance, participatory algorithmic auditing, and — most recently — generative-AI harms for creative workers.

Other publicly attributable work includes:

• The Community Reporting of Algorithmic System Harms (CRASH) project — originally launched in July 2020 as the Algorithmic Vulnerability Bounty Project — which prototypes mechanisms for ordinary people to discover, scope, and report harms produced by AI systems. The project's foundational report, Bug Bounties For Algorithmic Harms?, asked how the bug-bounty model from cybersecurity could be adapted to socio-technical harms. The project now feeds into AJL's live harms reporting platform, which aggregates community-submitted harm reports across categories including TSA facial recognition and generative-AI impacts on writers.
• The Freedom Flyers campaign, launched in 2023, which mobilizes air travelers to opt out of TSA facial recognition at U.S. airport checkpoints, document their experiences via a public scorecard, and feed those accounts into AJL's policy advocacy. The campaign's full Comply To Fly? report, released July 21, 2025 by Buolamwini, Sushma Raman, and Andrea Dean, drew on scorecards submitted across 91 airports between March 2024 and June 2025. Key findings: 99% of surveyed travelers were not given verbal notice of their right to opt out; 67% of those who did attempt to opt out reported verbal abuse, public shaming, and perceived additional scrutiny. The report recommended halting the TSA program for public deliberation and was cited in support of the bipartisan Traveler Privacy Protection Act (S. 1691, 119th Congress), introduced by Senators Kennedy, Merkley, Marshall, and Markey in 2025 to codify opt-out rights and restrict TSA's use of traveler biometric data.
• The #MyWorkMyRights campaign, an initiative collecting stories from writers whose livelihoods have been affected by generative AI — specifically, whose work has been used to train AI systems without consent, compensation, credit, or control. The campaign documents harms and feeds into AJL's policy advocacy for creators' rights, including linkage to the Authors Guild open letter. It extends AJL's participatory community-reporting model into the generative-AI era.
• The Drag vs AI workshop series, which uses drag performance and makeup techniques to teach participants how facial-recognition systems read identity, where those systems fail, and how everyday people can both critique and disrupt them. Drag vs AI is illustrative of AJL's distinctive register: cultural production as a way to widen the audience for technical and policy questions.
• A continuing stream of public-education output — talks, op-eds, library and curriculum resources — alongside Buolamwini's 2023 book Unmasking AI: My Mission to Protect What Is Human in a World of Machines, which doubles as a popular-press introduction to AJL's framing of the "coded gaze."

The 2020 documentary Coded Bias, directed by Shalini Kantayya, follows Buolamwini's research and AJL's early advocacy and remains a frequent on-ramp into the organization's work. In December 2025 AJL celebrated the film's fifth anniversary with a world-tour screening series in partnership with the University of Oxford's Institute of AI Ethics, culminating in a virtual finale at MIT on December 16, 2025. At the finale AJL presented the inaugural Global AI Justice Award to Megan L. Garcia, Esq., whose 14-year-old son Sewell Setzer III died after interacting with an AI chatbot companion; the award recognizes individuals who experienced AI harm and turned it into action to protect others.

Structure and funding

AJL is a U.S. nonprofit with a small staff and an extended bench of researcher and artist collaborators. Buolamwini is founder and president. Sasha Costanza-Chock, a sociologist and design scholar, has led the organization's research and design work, including the CRASH project. Tawana Petty, a longtime data-justice organizer, served as Director of Policy and Advocacy and represented AJL in U.S. and international AI-governance processes; precise current titles for staff beyond the founder are not always clearly disclosed and are tracked conservatively here. The July 2025 Comply To Fly? report credits Sushma Raman and Andrea Dean as co-authors alongside Buolamwini, indicating continued staff depth on research and policy. The December 2025 fifth-anniversary event identified Dr. Jaleesa Trapp (Senior Education Advisor) and Dr. Alexis Hope (AI and Work Advisor) as members of the AJL team.

AJL's work has been supported by the Ford, MacArthur, Mozilla, Rockefeller, and Alfred P. Sloan foundations, as well as the Democracy Fund, the Kapor Foundation, and individual donors.

Posture in the movement

AJL sits at a deliberate hinge between research institution and movement organization. Its outputs include peer-reviewed audits and policy briefs, but it equally prioritizes participatory artifacts — workshops, scorecards, opt-out campaigns, films, books — that engage non-specialists in the work of holding AI systems accountable. The organization's framing of the "coded gaze" has been adopted widely in public-facing AI-bias commentary and is one of the most legible cultural framings to come out of the broader make-AI-good movement. The Comply To Fly? report's legislative citation — via bipartisan Senate action and the U.S. Privacy and Civil Liberties Oversight Board — exemplifies AJL's strategy of grounding policy advocacy in community-sourced evidence. Its expansion into generative-AI harms, through the #MyWorkMyRights campaign, extends this model to a new harm domain without departing from AJL's characteristic register: participatory evidence-gathering, followed by public advocacy.