Joy Buolamwini | |
---|---|
![]() Buolamwini at Wikimania 2018 | |
Born | Joy Adowaa Buolamwini 23 January 1990 |
Education | Cordova High School |
Alma mater | Georgia Institute of Technology (BS) Jesus College, Oxford (MS) Massachusetts Institute of Technology (MS, PhD) |
Known for | Algorithmic Justice League |
Scientific career | |
Fields | Media Arts & Sciences Computer science Algorithmic bias |
Institutions | MIT Media Lab |
Theses | |
Doctoral advisor | Ethan Zuckerman [1] |
Website | www |
Joy Adowaa Buolamwini is a Canadian-American computer scientist and digital activist formerly based at the MIT Media Lab. [2] She founded the Algorithmic Justice League (AJL), an organization that works to challenge bias in decision-making software, using art, advocacy, and research to highlight the social implications and harms of artificial intelligence (AI). [3] [4]
Buolamwini was born in Edmonton, Alberta, grew up in Mississippi, and attended Cordova High School in Cordova, Tennessee. [5] At age nine, she was inspired by Kismet, the MIT robot, and taught herself XHTML, JavaScript and PHP. [6] [7] As a student-athlete, she was a competitive pole vaulter [8] and played basketball. In a podcast episode she recorded on Brené Brown's show "Dare to Lead", she recalls completing her AP Physics homework between basketball break times. [9]
As an undergraduate, Buolamwini studied computer science at the Georgia Institute of Technology, where she researched health informatics. [10] Buolamwini graduated as a Stamps President's Scholar [11] from Georgia Tech in 2012, [12] and was the youngest finalist of the Georgia Tech InVenture Prize in 2009. [13]
Buolamwini is a Rhodes Scholar, a Fulbright fellow, a Stamps Scholar, an Astronaut Scholar, and an Anita Borg Institute scholar. [14] As a Rhodes Scholar, she studied learning and technology at the University of Oxford, where she was a student based at Jesus College, Oxford. [15] [16] During her scholarship she took part in the first formal Service Year, working on community focused projects. [16] [17] She was awarded a Master's Degree in Media Arts & Sciences from MIT in 2017 for research supervised by Ethan Zuckerman. [1] She was awarded a PhD degree in Media Arts & Sciences from the MIT Media Lab in 2022 with a thesis on Facing the Coded Gaze with Evocative Audits and Algorithmic Audits. [18]
In 2011, Buolamwini worked with the trachoma program at the Carter Center to develop an Android-based assessment system for use in Ethiopia. [19] [6]
As a Fulbright fellow, in 2013 she worked with local computer scientists in Zambia to help Zambian youth become technology creators. [20] On September 14, 2016, Buolamwini appeared at the White House summit on Computer Science for All. [21]
Buolamwini was a researcher at the MIT Media Lab, where she worked to identify bias in algorithms and to develop practices for accountability during their design; [22] at the lab, Buolamwini was a member of Ethan Zuckerman's Center for Civic Media group. [23] [24] During her research, Buolamwini showed 1,000 faces to facial recognition systems and asked the systems to identify whether faces were female or male, and found that the software found it hard to identify dark-skinned women. [25] Her project, Gender Shades, became part of her MIT thesis. [1] [26] Her 2018 paper Gender Shades: Intersectional Accuracy Disparities in Commercial Gender Classification [27] prompted responses from IBM and Microsoft to take corrective actions to improve the accuracy of their algorithms, swiftly improved their software demonstrating her influence on the industry. [28] [29] She also created the Aspire Mirror, a device that lets users see a reflection of themselves based on what inspires them. [30] Her program, Algorithmic Justice League, aims to highlight the bias in code that can lead to discrimination against underrepresented groups. [31] She has created two films, Code4Rights and Algorithmic Justice League: Unmasking Bias. [32] [33] Still the director, Code4rights is an advocacy organization started in 2012 intended to use technology to spread awareness of human rights. [34] She served as Chief Technology Officer (CTO) for Techturized Inc., a hair-care technology company. [10]
Buolamwini's research was cited in 2020 as an influence for Google and Microsoft in addressing gender and race bias in their products and processes. [35]
She also served as an advisor to President Biden ahead of his administration's Executive Order 14110, released October 30, 2023. The order is also known as the Executive Order on Safe, Secure, and Trustworthy Development and Use of Artificial Intelligence (sometimes referred to as "Executive Order on Artificial Intelligence"). [36] [37]
In 2023, she published her first book, Unmasking AI: My Mission to Protect What Is Human in a World of Machines, which chronicles her research. [38] Through her book:
Dr. Joy Buolamwini’s research on AI bias has been pivotal in advancing gender equity within engineering and technology. Her research found that AI-powered facial-recognition systems showed higher error rates when identifying darker-skinned women, with rates reaching 34.7%, compared to 0.8% for lighter-skinned men. These disparities indicated potential biases in algorithmic design, where biased training data and incomplete evaluation processes led to unequal technological outcomes based on both gender and skin tone. [42]
Buolamwini’s personal experience with AI performance limitations motivated her research into algorithmic bias. While working on a facial-recognition-based art project at the MIT Media Lab, she discovered that commercial AI systems could not consistently detect her face due to her darker skin. This frustration inspired her landmark research project Gender Shades, which rigorously evaluated facial analysis systems from IBM, Microsoft, and Face++. Her study revealed that these systems were most accurate for lighter-skinned men, with error rates as low as 1%, while their accuracy plummeted for darker-skinned women, with misclassification rates as high as 47%. [43]
Realizing that these failures stemmed from data imbalances, Buolamwini introduced the Pilot Parliaments Benchmark, a diverse dataset designed to address the lack of representation in typical AI training sets, which were composed of over 75% male and 80% lighter-skinned faces. This new benchmark set a critical precedent for evaluating and improving AI performance by ensuring more equitable testing standards. [43]
Her findings contributed to significant changes in the tech industry. Following the publication of her research, companies such as IBM and Microsoft took steps to improve their algorithms, reducing bias and enhancing accuracy. However, Buolamwini has noted that improved technical accuracy alone does not eliminate risks of potential misuse in areas such as racial profiling, surveillance, and hiring decisions. [43]
To address these concerns, Buolamwini co-founded the Safe Face Pledge, encouraging tech companies to adopt ethical AI practices. The pledge prohibits weaponizing facial recognition, bans lawless police use, and demands transparency in government surveillance applications. Her advocacy emphasizes that achieving fairness in AI development requires a multi-faceted approach, including regulatory frameworks and collaborative efforts. [43]
Buolamwini founded the Algorithmic Justice League (AJL) in 2016 to promote equitable and accountable artificial intelligence (AI). [44] The AJL organization integrates art and research to examine societal implications and reduce AI-related harms. The company works to raise public awareness of AI’s impact while advancing research on bias mitigation. It also addresses issues at the intersection of equity and technology, promoting more inclusive and accessible engineering systems. AJL has also encouraged public engagement through interactive campaigns, exhibitions, and educational initiatives, ensuring a broad audience is informed about the impact of biased algorithms on gender equity.
To broaden its outreach, AJL has partnered with organizations such as Black Girls Code to encourage African-American girls to pursue STEM careers, thereby fostering more diversity in the tech industry. AJL conducts workshops and provides resources aimed at educating the public and tech community about AI biases, with a focus on empowering underrepresented genders to engage with and challenge these systems.
The success of AJL reflects the collective efforts of its team. Some key members of the Algorithmic Justice League include Rachel Fagen, the Chief of Staff, who focuses on organizational development and building connections to promote equitable and accountable AI. Aurum Linh serves as the AI Harms Analyst, dedicated to identifying and mitigating the adverse effects of artificial intelligence. The Algorithm Justice League works with various groups, including CORE funders, advisory committees, and research collaborators, to enhance transparency and accountability in AI systems, ensuring that its advocacy efforts remain impactful and inclusive. [45]
The AJL website provides information and a live blog. [46] There are several sections on the site where users can share stories, and donate or write to US Congressional representatives. Buolamwini has influenced policy discussions to address gender discrimination in AI applications, advocating for regulations that ensure fairness in AI-powered decision-making systems. In 2019, she testified before the United States House Committee on Oversight and Reform about the risks of facial recognition technology. [47] Her testimony emphasized the need for accountability in the deployment of facial recognition technologies, particularly in areas where these systems could exacerbate gender inequities.
She believed the executive order fell short in terms of redress, or consequences, for AI systems that hurt minority communities. [48] Her efforts supported the inclusion of measures to address discrimination in AI applications, particularly in areas like hiring, housing, and criminal justice. Biden’s executive order is a “long and ongoing process” which is happening because the industry is not incentivized to do so she said. [49]
Joy Buolamwini, through the Algorithmic Justice League (AJL), has been instrumental in advocating for the inclusion and support of women, transgender, and non-binary individuals in the technology sector. Her initiatives focus on exposing and mitigating biases in artificial intelligence (AI) that disproportionately affect these underrepresented groups.
Buolamwini has led campaigns targeting gender equity in AI and technology. In 2021, she collaborated with Olay on the Decode the Bias campaign, which examined biases in beauty algorithms affecting women of color. This initiative evaluated Olay's Skin Advisor System to ensure equitable treatment across all skin tones. [50]
Building on these initiatives, AJL launched the Community Reporting of Algorithmic System Harms (CRASH), which unites key stakeholders to develop tools that enable broader participation in creating accountable and equitable AI systems, directly addressing issues that affect underrepresented genders. [51]
The Voicing Erasure section on the AJL website hosts spoken pieces by Buolamwini, Allison Koenecke, Safiya Noble, Ruha Benjamin, Kimberlé Crenshaw, Megan Smith, and Sasha Costanza-Chock about bias in voice systems. [52] [53] Buolamwini and Koenecke are the lead researchers on the website working to uncovering biases of voice systems. They've written that speech recognition systems have the most trouble with African-American Vernacular English speakers, and that these systems are secretly listening to users' conversations. [54] They have also written about what they regard as harmful gender stereotypes perpetuated by the voice recognition systems in Siri, Amazon Alexa, and Microsoft Cortana. [55]
While her methodology and results have faced criticism from industries like Amazon, she explained in her TED talk how she addressed the 'coded gaze' by highlighting its neglect of the intersection between “social impact, technology, and inclusion. [56]
Her Voicing Erasure project highlights gender equity by exposing biases in voice recognition systems, particularly those that often fail to accurately process speech from women and non-binary individuals. This project advocates for more inclusive AI development by raising awareness of these limitations. [57]
The Coded Gaze is a mini-documentary that debuted at the Museum of Fine Arts, Boston in 2016, and is currently available via YouTube. Buolamwini uses the mini documentary to talk about the bias that she believes lies in artificial intelligence's function. The inspiration for the mini documentary and her research came when she was at MIT, creating her art "Aspire Mirror," which uses facial recognition to reflect another person who inspires a user, onto that user's face. [58] Buolamwini anticipated having Serena Williams, another dark-skinned woman, reflected onto her face. However, the technology did not recognize her face. Buolamwini's research investigated why this happened, and consequently led Buolamwini to conclude that the exclusion of people who look like her was a result of a practice she called the "Coded Gaze." [59] She further discusses this concept in the mini documentary, "The Coded Gaze." The documentary explores how AI can be subject to racial and gender biases that reflect the views and cultural backgrounds of those who develop it. [60]
Coded Bias is a documentary film directed by Shalini Kantayya that features Buolamwini’s research about AI inaccuracies in facial recognition technology and automated assessment software. [61] [46] It focuses on what the film's creators regard as a lack of regulation of facial recognition tools sold by IBM, Microsoft, and Amazon, and which they say perpetuates racial and gender bias. The film describes a dispute between Brooklyn tenants and a building management company that tried to use facial recognition to control entry to a building. The film featured Weapons of Math Destruction author Cathy O'Neill and members of Big Brother Watch in London, including Silkie Carlo. On April 5, 2021, the documentary was made available to stream on Netflix. [62]
Projects conducted by Algorithmic Justice League have been exhibited at art institutions including the Barbican Centre in London, UK, and Ars Electronica in Linz, Austria. [63]
In 2017, Buolamwini was awarded the grand prize in the professional category in the Search for Hidden Figures contest, tied to the release of the film Hidden Figures in December 2016. [69] The contest, sponsored by PepsiCo and 21st Century Fox, was intended to "help uncover the next generation of female leaders in science, technology, engineering and math," [70] and attracted 7,300 submissions from young women across the United States. [12]
Buolamwini delivered a TEDx talk at Beacon Street entitled How I'm fighting bias in algorithms. [71] [72] [73] In 2018, she appeared on the TED Radio Hour. [74] She was featured on Amy Poehler's Smart Girls in 2018. [5] Fast Company magazine listed her as one of four "design heroes who are defending democracy online." [75] She was listed as one of BBC's 100 Women in 2018. [76]
In 2019, Buolamwini was listed in Fortune magazine's 2019 list of the "World's 50 Greatest Leaders," where the magazine described her as "the conscience of the A.I. revolution." [77] She also made the inaugural Time 100 Next list in 2019. [78] In 2020, Buolamwini featured in a women's empowerment campaign by the clothing company Levi's for International Women's Day. [79] She was also featured in the documentary Coded Bias. [80] In 2020, an honoree of the Great Immigrants Award named by Carnegie Corporation of New York. [81]
In 2022, Buolamwini was named the ASQ Hutchens Medalist. [82] In 2023, she was listed in the Time 100 AI. [83]
In June 9, 2024, Buolamwini was awarded an honorary Doctor of Science degree from Dartmouth College for her work in exposing biases in AI systems and preventing AI harms. She was also invited as the keynote speaker for Dartmouth's 2024 Social Justice Awards. [84]
Buolamwini has lived in Ghana; Barcelona, Spain; Oxford, United Kingdom; and, in the U.S., Memphis, Tennessee, and Atlanta, Georgia. [13] She describes herself as a "daughter of the science and of the arts", [9] her father being an Academic and her mother an artist, as well as a Poet of Code. [9]
A facial recognition system is a technology potentially capable of matching a human face from a digital image or a video frame against a database of faces. Such a system is typically employed to authenticate users through ID verification services, and works by pinpointing and measuring facial features from a given image.
Kate Crawford is a researcher, writer, composer, producer and academic, who studies the social and political implications of artificial intelligence. She is based in New York and works as a principal researcher at Microsoft Research, the co-founder and former director of research at the AI Now Institute at NYU, a visiting professor at the MIT Center for Civic Media, a senior fellow at the Information Law Institute at NYU, and an associate professor in the Journalism and Media Research Centre at the University of New South Wales. She is also a member of the WEF's Global Agenda Council on Data-Driven Development.
DeepFace is a deep learning facial recognition system created by a research group at Facebook. It identifies human faces in digital images. The program employs a nine-layer neural network with over 120 million connection weights and was trained on four million images uploaded by Facebook users. The Facebook Research team has stated that the DeepFace method reaches an accuracy of 97.35% ± 0.25% on Labeled Faces in the Wild (LFW) data set where human beings have 97.53%. This means that DeepFace is sometimes more successful than human beings. As a result of growing societal concerns Meta announced that it plans to shut down Facebook facial recognition system, deleting the face scan data of more than one billion users. This change will represent one of the largest shifts in facial recognition usage in the technology's history. Facebook planned to delete by December 2021 more than one billion facial recognition templates, which are digital scans of facial features. However, it did not plan to eliminate DeepFace which is the software that powers the facial recognition system. The company has also not ruled out incorporating facial recognition technology into future products, according to Meta spokesperson.
Labeled data is a group of samples that have been tagged with one or more labels. Labeling typically takes a set of unlabeled data and augments each piece of it with informative tags. For example, a data label might indicate whether a photo contains a horse or a cow, which words were uttered in an audio recording, what type of action is being performed in a video, what the topic of a news article is, what the overall sentiment of a tweet is, or whether a dot in an X-ray is a tumor.
Algorithmic bias describes systematic and repeatable errors in a computer system that create "unfair" outcomes, such as "privileging" one category over another in ways different from the intended function of the algorithm.
Meredith Broussard is a data journalism professor at the Arthur L. Carter Journalism Institute at New York University. Her research focuses on the role of artificial intelligence in journalism.
Meredith Whittaker is the president of the Signal Foundation and serves on its board of directors. She was formerly the Minderoo Research Professor at New York University (NYU), and the co-founder and faculty director of the AI Now Institute. She also served as a senior advisor on AI to Chair Lina Khan at the Federal Trade Commission. Whittaker was employed at Google for 13 years, where she founded Google's Open Research group and co-founded the M-Lab. In 2018, she was a core organizer of the Google Walkouts and resigned from the company in July 2019.
Timnit Gebru is an Eritrean Ethiopian-born computer scientist who works in the fields of artificial intelligence (AI), algorithmic bias and data mining. She is a co-founder of Black in AI, an advocacy group that has pushed for more Black roles in AI development and research. She is the founder of the Distributed Artificial Intelligence Research Institute (DAIR).
Amazon Rekognition is a cloud-based software as a service (SaaS) computer vision platform that was launched in 2016. It has been sold to, and used by, a number of United States government agencies, including U.S. Immigration and Customs Enforcement (ICE) and Orlando, Florida police, as well as private entities.
Rachel Thomas is an American computer scientist and founding Director of the Center for Applied Data Ethics at the University of San Francisco. Together with Jeremy Howard, she is co-founder of fast.ai. Thomas was selected by Forbes magazine as one of the 20 most incredible women in artificial intelligence.
Coded Bias is an American documentary film directed by Shalini Kantayya that premiered at the 2020 Sundance Film Festival. The film includes contributions from researchers Joy Buolamwini, Deborah Raji, Meredith Broussard, Cathy O’Neil, Zeynep Tufekci, Safiya Noble, Timnit Gebru, Virginia Eubanks, and Silkie Carlo, and others.
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 uses research, artwork, and policy advocacy to increase societal awareness regarding the use of artificial intelligence (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, Fast Company named AJL as one of the 10 most innovative AI companies in the world.
Rashida Richardson is a visiting scholar at Rutgers Law School and the Rutgers Institute for Information Policy and the Law and an attorney advisor to the Federal Trade Commission. She is also an assistant professor of law and political science at the Northeastern University School of Law and the Northeastern University Department of Political Science in the College of Social Sciences and Humanities.
Inioluwa Deborah Raji is a Nigerian-Canadian computer scientist and activist who works on algorithmic bias, AI accountability, and algorithmic auditing. Raji has previously worked with Joy Buolamwini, Timnit Gebru, and the Algorithmic Justice League on researching gender and racial bias in facial recognition technology. She has also worked with Google’s Ethical AI team and been a research fellow at the Partnership on AI and AI Now Institute at New York University working on how to operationalize ethical considerations in machine learning engineering practice. A current Mozilla fellow, she has been recognized by MIT Technology Review and Forbes as one of the world's top young innovators.
Karen Hao is an American journalist and data scientist. Currently a contributing writer for The Atlantic and previously a foreign correspondent based in Hong Kong for The Wall Street Journal and senior artificial intelligence editor at the MIT Technology Review, she is best known for her coverage on AI research, technology ethics and the social impact of AI. Hao also co-produces the podcast In Machines We Trust and writes the newsletter The Algorithm.
Virginia Eubanks is an American political scientist, professor, and author studying technology and social justice. She is an associate professor in the Department of Political Science at the University at Albany, SUNY. Previously Eubanks was a Fellow at New America researching digital privacy, economic inequality, and data-based discrimination.
Tawana Petty is an American author, poet, social justice organizer, mother and youth advocate who works to counter systemic racism. Petty formerly served as Director of Policy and Advocacy for the Algorithmic Justice League representing AJL in national and international processes shaping AI governance.
Allison Koenecke is an American computer scientist and an assistant professor in the Department of Information Science at Cornell University. Her research considers computational social science and algorithmic fairness. In 2022, Koenecke was named one of the Forbes 30 Under 30 in Science.
Discussions on regulation of artificial intelligence in the United States have included topics such as the timeliness of regulating AI, the nature of the federal regulatory framework to govern and promote AI, including what agency should lead, the regulatory and governing powers of that agency, and how to update regulations in the face of rapidly changing technology, as well as the roles of state governments and courts.
Mutale Nkonde is a Zambian journalist and artificial intelligence policy researcher. She founded the nonprofit, AI for the People, aimed at reducing algorithmic bias.
{{cite web}}
: CS1 maint: numeric names: authors list (link){{cite web}}
: CS1 maint: multiple names: authors list (link){{cite web}}
: CS1 maint: numeric names: authors list (link)