---
title: "Algorithmic Justice League"
chunk: 1/2
source: "https://en.wikipedia.org/wiki/Algorithmic_Justice_League"
category: "reference"
tags: "science, encyclopedia"
date_saved: "2026-05-05T04:22:14.048812+00:00"
instance: "kb-cron"
---

The Algorithmic Justice League (AJL) is a digital advocacy non-profit organization based in Cambridge, Massachusetts. Founded in 2016 by computer scientist Joy Buolamwini, the AJL combats the harms and biases of AI by amplifying the voices of communities most affected by AI systems and mobilizing advocates, researchers, and industry leaders to build more equitable and accountable algorithms. The AJL has engaged in a variety of open online seminars, media appearances, and tech advocacy initiatives to communicate information about bias in AI systems and promote industry and government action to mitigate against the creation and deployment of biased AI systems. Most notably, AJL has highlighted racial and gender biases in facial recognition technologies and has advocated for greater transparency and accountability in the datasets used to train these systems. In 2021, Fast Company named AJL as one of the 10 most innovative AI companies in the world.

== History ==
Buolamwini founded the Algorithmic Justice League in 2016 as a graduate student in the MIT Media Lab. While experimenting with facial detection software in her research, she found that the software could not detect her "highly melanated" face until she donned a white mask. After this incident, Buolamwini became inspired to found AJL to draw public attention to the existence of bias in artificial intelligence and the threat it can poses to civil rights. Early AJL campaigns focused primarily on bias in face recognition software; recent campaigns have dealt more broadly with questions of equitability and accountability in AI, including algorithmic bias, algorithmic decision-making, algorithmic governance, and algorithmic auditing.
Additionally there is a community of other organizations working towards similar goals, including Data and Society, Data for Black Lives, the Distributed Artificial Intelligence Research Institute (DAIR), and Fight for the Future.

== Notable work ==

=== Facial recognition ===
AJL founder Buolamwini collaborated with AI ethicist Timnit Gebru to release a 2018 study on racial and gender bias in facial recognition algorithms used by commercial systems from Microsoft, IBM, and Face++. Their research, entitled "Gender Shades", determined that machine learning models released by IBM and Microsoft were less accurate when analyzing dark-skinned and feminine faces compared to performance on light-skinned and masculine faces. The "Gender Shades" paper was accompanied by the launch of the Safe Face Pledge, an initiative designed with the Georgetown Center on Privacy & Technology that urged technology organizations and governments to prohibit lethal use of facial recognition technologies. The Gender Shades project and subsequent advocacy undertaken by AJL and similar groups led multiple tech companies, including Amazon and IBM, to address biases in the development of their algorithms and even temporarily ban the use of their products by police in 2020.
Buolamwini and AJL were featured in the 2020 Netflix documentary Coded Bias, which premiered at the Sundance Film Festival. This documentary focused on the AJL's research and advocacy efforts to spread awareness of algorithmic bias in facial recognition systems.
A research collaboration involving AJL released a white paper in May 2020 calling for the creation of a new United States federal government office to regulate the development and deployment of facial recognition technologies. The white paper proposed that creating a new federal government office for this area would help reduce the risks of mass surveillance and bias posed by facial recognition technologies towards vulnerable populations.

=== Bias in speech recognition ===
The AJL has run initiatives to increase public awareness of algorithmic bias and inequities in the performance of AI systems for speech and language modeling across gender and racial populations. The AJL's work in this space centers on highlighting gender and racial disparities in the performance of commercial speech recognition and natural language processing systems, which have been shown to underperform on racial minorities and reinforced gender stereotypes.
In March 2020, AJL released a spoken word artistic piece, titled Voicing Erasure, that increased public awareness of racial bias in automatic speech recognition (ASR) systems. The piece was performed by numerous female and non-binary researchers in the field, including Ruha Benjamin, Sasha Costanza-Chock, Safiya Noble, and Kimberlé Crenshaw. AJL based their development of "Voicing Erasure" on a 2020 PNAS paper, titled, "Racial disparities in automated speech recognition" that identified racial disparities in performance of five commercial ASR systems.

=== Algorithmic governance ===
In 2019, Buolamwini represented AJL at a congressional hearing of the US House Committee on Science, Space, and Technology, to discuss the applications of facial recognition technologies commercially and in the government. Buolamwini served as a witness at the hearing and spoke on underperformance of facial recognition technologies in identifying people with darker skin and feminine features and supported her position with research from the AJL project "Gender Shades".
In January 2022, the AJL collaborated with Fight for the Future and the Electronic Privacy Information Center to release an online petition called DumpID.me, calling for the IRS to halt their use of ID.me, a facial recognition technology they were using on users when they log in. The AJL and other organizations sent letters to legislators and requested them to encourage the IRS to stop the program. In February 2022, the IRS agreed to halt the program and stop using facial recognition technology. AJL has now shifted efforts to convince other government agencies to stop using facial recognition technology; as of March 2022, the DumpID.me petition has pivoted to stop the use of ID.me in all government agencies.