295:, 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.
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143:. 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
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programs that compensate and encourage individuals to locate and disclose the existence of bias in AI systems. AJL intends for the CRASH framework to give individuals the ability to report algorithmic harms and stimulate change in AI technologies deployed by companies, especially individuals who have traditionally been excluded from the design of these AI technologies .
207:. 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
125:(AI) in society and the harms and biases that AI can pose to society. 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. In 2021,
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commitment to obtaining customer consent for their selfies and skin data to be used in this audit. The AJL and ORCAA audit revealed that the OSA system contained bias in its performance across participants' skin color and age. The OSA system demonstrated higher accuracy for participants with lighter skin tones, per the
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collaborated with AJL and O'Neil Risk
Consulting & Algorithmic Auditing (ORCAA) to conduct the Decode the Bias campaign, which included an audit that explored whether the Olay Skin Advisor (OSA) System included bias against women of color. The AJL chose to collaborate with Olay due to Olay's
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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
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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
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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
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programs (BBPs) that would incentivize individuals to uncover and report instances of algorithmic bias in AI technologies. After conducting interviews with BBP participants and a case study of
Twitter's BBP program, AJL researchers developed and proposed a conceptual framework for designing BBP
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Skin Type and individual typology angle skin classification scales. The OSA system also demonstrated higher accuracy for participants aged 30–39. Olay has, since, taken steps to internally audit and mitigate against the bias of the OSA system. Olay has also funded 1,000 girls to attend the
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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
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271:. 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.
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In July 2020, the
Community Reporting of Algorithmic System Harms (CRASH) Project was launched by AJL. This project began in 2019 when Buolamwini and digital security researcher
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Bender, Emily M.; Gebru, Timnit; McMillan-Major, Angelina; Shmitchell, Shmargaret (March 3, 2021). "On the
Dangers of Stochastic Parrots: Can Language Models be Too Big?".
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facial recognition technologies in identifying people with darker skin and feminine features and supported her position with research from the AJL project "Gender Shades".
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In March 2020, AJL released a spoken word artistic piece, titled
Voicing Erasure, that increased public awareness of racial bias in automatic
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this area would help reduce the risks of mass surveillance and bias posed by facial recognition technologies towards vulnerable populations.
223:. This documentary focused on the AJL's research and advocacy efforts to spread awareness of algorithmic bias in facial recognition systems.
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software; recent campaigns have dealt more broadly with questions of equitability and accountability in AI, including
1329:"We listened to more than 3 hours of US Congress testimony on facial recognition so you didn't have to go through it"
988:"Biometrics experts call for creation of FDA-style government body to regulate facial recognition | Biometric Update"
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Additionally there is a community of other organizations working towards similar goals, including Data and
Society,
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to release a 2018 study on racial and gender bias in facial recognition algorithms used by commercial systems from
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255:(ASR) systems. The piece was performed by numerous female and non-binary researchers in the field, including
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systems, which have been shown to underperform on racial minorities and reinforced gender stereotypes.
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recognized AJL as one of the 10 most innovative AI companies in 2021. Additionally, venues such as
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1122:. Proceedings of the 7th Joint Conference on Lexical and Computational Semantics. pp. 43–53.
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1556:"Olay Teams Up With Algorithmic Justice Pioneer Joy Buolamwini To #DecodetheBias In Beauty"
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Buolamwini founded the
Algorithmic Justice League in 2016 as a graduate student in the MIT
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673:"Activists pushed the IRS to drop facial recognition. They won, but they're not done yet"
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601:"Google fired its star AI researcher one year ago. Now she's launching her own institute"
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to release an online petition called DumpID.me, calling for the IRS to halt their use of
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877:"IBM pulls out of facial recognition, fearing racial profiling and mass surveillance"
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Proceedings of the 2021 ACM Conference on
Fairness, Accountability, and Transparency
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Miller, Erik
Learned; Ordóñez, Vicente; Morgenstern, Jamie; Buolamwini, Joy (2020).
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1397:"Dozens of advocacy groups push for Congress to ban facial recognition technology"
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Proceedings of the Seventh Joint Conference on Lexical and Computational Semantics
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have featured Buolamwini's work with the AJL in several interviews and articles.
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algorithms and even temporarily ban the use of their products by police in 2020.
847:"The two-year fight to stop Amazon from selling face recognition to the police"
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1458:"IRS halts plan to require facial recognition for logging in to user accounts"
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Proceedings of the 1st Conference on Fairness, Accountability and Transparency
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337:. The CRASH project focused on creating the framework for the development of
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1856:"Joy Buolamwini: How Do Biased Algorithms Damage Marginalized Communities?"
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1113:"Examining Gender and Race Bias in Two Hundred Sentiment Analysis Systems"
1019:"Facial Recognition Technologies in the Wild: A Call for a Federal Office"
1776:"Bounty Everything: Hackers and the Making of the Global Bug Marketplace"
1298:"Algorithmic Justice League protests bias in voice AI and media coverage"
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571:"Documentary 'Coded Bias' Unmasks The Racism Of Artificial Intelligence"
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named AJL as one of the 10 most innovative AI companies in the world.
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1427:"U.S. government study finds racial bias in facial recognition tools"
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1650:"Bug Bounties For Algorithmic Harms? | Algorithmic Justice League"
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camp, to encourage African-American girls to pursue STEM careers.
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Buolamwini and AJL were featured in the 2020 Netflix documentary
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789:"MIT Researcher: AI Has a Race Problem, and We Need to Fix It"
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515:"The 10 most innovative companies in artificial intelligence"
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1518:"Decode the Bias & Face Anything | Women in STEM | OLAY"
1366:"Artificial Intelligence: Societal and Ethical Implications"
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The AJL has run initiatives to increase public awareness of
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1072:. FAccT '21. Virtual Event Canada: ACM. pp. 610–623.
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113:) is a digital advocacy non-profit organization based in
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met at the Bellagio Center Residency Program, hosted by
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Distributed Artificial Intelligence Research Institute
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Existential risk from artificial general intelligence
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AJL founder Buolamwini collaborated with AI ethicist
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Civil liberties advocacy groups in the United States
1196:"Racial disparities in automated speech recognition"
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Social welfare charities based in the United States
1774:Ellis, Ryan Ellis; Stevens, Yuan (January 2022).
1619:"Algorithmic Vulnerability Bounty Project (AVBP)"
1111:Kiritchenko, Svetlana; Mohammad, Saif M. (2018).
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1370:House Committee on Science, Space and Technology
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1954:Non-profit organizations based in Massachusetts
1944:Organizations based in Cambridge, Massachusetts
1200:Proceedings of the National Academy of Sciences
1743:League, Algorithmic Justice (August 4, 2021).
548:"Coded Bias and the Algorithm Justice League"
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209:Georgetown Center on Privacy & Technology
1810:: CS1 maint: multiple names: authors list (
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1488:"Demand All Government Agencies Drop ID.me"
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283:In January 2022, the AJL collaborated with
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908:"When Bias Is Coded Into Our Technology"
350:AJL initiatives have been funded by the
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1964:Data and information organizations
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1745:"Happy Hacker Summer Camp Season!"
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1691:from the original on April 8, 2022
1679:Vincent, James (August 10, 2021).
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823:. January 20, 2021. Archived from
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486:. The Algorithmic Justice League.
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821:"Announcement - Safe Face Pledge"
768:from the original on May 29, 2022
424:Ethics of artificial intelligence
1924:Ethics of science and technology
1648:Laas, Molly (January 27, 2022).
1456:Rachel Metz (February 7, 2022).
1327:Quach, Katyanna (May 22, 2019).
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986:Burt, | Chris (June 8, 2020).
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671:Metz, Rachel (March 7, 2022).
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1826:"AJL Bug Bounties Report.pdf"
1711:"AJL Bug Bounties Report.pdf"
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459:Margaret Mitchell (scientist)
346:Support and media appearances
299:Olay Decode the Bias campaign
1974:Social justice organizations
1909:Digital rights organizations
957:"Watch Coded Bias | Netflix"
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1163:– via June 5–6, 2018.
429:Fairness (machine learning)
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246:natural language processing
153:algorithmic decision-making
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1889:Algorithmic Justice League
360:Alfred P. Sloan Foundation
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1079:10.1145/3442188.3445922
992:www.biometricupdate.com
123:artificial intelligence
1929:Diversity in computing
364:Rockefeller Foundation
275:Algorithmic governance
221:Sundance Film Festival
157:algorithmic governance
1433:. December 19, 2019.
851:MIT Technology Review
577:. November 18, 2020.
1492:Fight for the Future
1138:10.18653/v1/S18-2005
449:Sasha Costanza-Chock
356:MacArthur Foundation
335:Sasha Costanza-Chock
285:Fight for the Future
261:Sasha Costanza-Chock
176:Fight for the Future
168:Data for Black Lives
1212:2020PNAS..117.7684K
827:on January 20, 2021
606:The Washington Post
303:In September 2021,
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1934:Information ethics
383:The New York Times
368:Mozilla Foundation
253:speech recognition
242:speech recognition
187:Facial recognition
159:, and algorithmic
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1372:. June 26, 2019.
1304:. April 1, 2020.
1265:"Voicing Erasure"
1206:(14): 7684–7689.
1192:Koenecke, Allison
1089:978-1-4503-8309-7
523:. March 9, 2021.
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