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, and community-reporting infrastructure. 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, and participatory algorithmic auditing more broadly.

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 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 associated Comply To Fly? report has been cited in U.S. Commission on Civil Rights testimony.
• 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.

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.

According to publicly reported coverage of AI-philanthropy giving, AJL's work has been supported by the Ford, MacArthur, Rockefeller, Alfred P. Sloan, and Mozilla foundations, alongside individual donors. Funder entities are not yet drafted in this corpus and are tracked as follow-ups rather than enumerated in the frontmatter.

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.