In honor of #BlackinDataWeek, the Sadie Collective has a list of nine Black women data scientists to know, including HST MEMP PhD student Jordan Harrod.
Olusayo Adeleye | Funke Aderonmu
Members of the Sadie Collective, Adeleye is a Fellow at the Ph.D. Excellence Initiative at NYU and Aderonmu is a policy analyst. The Sadie Collective is an organization that addresses the pipeline and pathway problem for Black women in economics, finance, data science and public policy through curated content creation, programming and mentorship.
It’s been said that data is the new oil, having the potential to totally transform the way society operates in the future. Organizations routinely seek novel ways to capture and glean insights from data, and it’s increasingly being used to shape policy and decision-making across public and private sectors. Given data’s growing prominence in all aspects of human endeavor, access to the data science field is key for would-be professionals and changemakers.
Despite this, entry into the data science field has been severely unequal, particularly along racial and gender lines. In the wake of a global reckoning with the effects of systemic racism, economic and social injustice, the field of data science offers a valuable opportunity to meaningfully advance equity and inclusion. Doing so is critical to ensuring that the future benefits of a data-driven world are shared and leveraged equitably to solve societal problems rather than exacerbate them.
The inclusion and diversity challenges facing data science have roots early in the pipeline. Many people enter the field at the undergraduate level with majors like computer science and mathematics. As the field has expanded in the 2000s and 2010s, we’ve seen a general increase in students majoring in these areas. From 2006 to 2017, there was a 61 percent increase in students who earned bachelor’s degrees in mathematics and a 69 percent increase for computer science degrees. Despite this growth, the number of Black women who graduated with degrees in computer science or mathematics increased at a much slower rate over the same period. This trend suggests that Black women are being left behind at the undergraduate level in data science.
Data taken from IPEDS. Graphic created by Olusayo Adeleye.
Data taken from IPEDS. Graphic created by Olusayo Adeleye.
Indeed, major disparities exist in the data science field, particularly for Black women. Recent estimates show that Black people make up just 3 percent of data and analytics professionals, and women overall make up only 15 percent of data scientists. Such paucity of diversity likely means Black women are largely missing from data science. A number of barriers, including inadequate STEM education and mentorship opportunities as well as bias in recruitment and exclusionary work cultures, serve to keep Black women and other underrepresented groups from entering data science careers.
Black women’s underrepresentation in data science has serious implications for how insights from the field influence society, particularly with respect to racial justice and equity. A growing body of evidence demonstrates that algorithms and artificial intelligence (AI) driven by data science can be embedded with racial, gender and other forms of bias. These patterns of bias stem in part from a lack of diversity in the broader data science field. Left unaddressed, these problems risk replicating and exacerbating existing inequalities.
Despite the current reality, Black women have nevertheless been making strides in data science, working to expand access to the field for their peers and those coming behind them. Additionally, Black-led organizations centering data-driven careers, such as the Sadie Collective, Black in Data, Black in AI and Data for Black Lives have emerged to address many of the barriers Black women and other underrepresented groups face to entering the data science field.
In honor of #BlackinDataWeek and the launch of the 2021 Sadie T.M. Alexander Conference for Economics and Related Fields, here is a list of nine Black women who are leading and promoting equity in the world of data science and beyond. These women deserve recognition for their work in promoting equity and inclusion in data science.
Rediet Abebe, Black in AI
Rediet Abebe holds a doctorate in computer science from Cornell University. She is a junior fellow at the Harvard Society of Fellows and an assistant professor of computer science at the University of California–Berkeley. Her scholarly work addresses issues of inequality in artificial intelligence and algorithms. She co-founded Mechanism Design for Social Good (MD4SG), a research collective interested in building algorithms that will open new doors for traditionally underrepresented groups. Her work was shaped by her upbringing in Addis Ababa, Ethiopia, and has had a tangible impact on the world, including through her collaborations with the National Institutes of Health and Ethiopian Ministry of Education.
Dominique Harrison, Aspen Digital
Dominique Harrison holds a doctorate in communications policy and serves a project director at the Aspen Institute in the Aspen Digital Program. She helps create tech policies and initiatives that will lead to a more inclusive and just society. Dr. Harrison directs the Tech X Talent Project, which promotes equity in the technology industry. She also manages the Institute's Firestone Fellowship, which empowers doctoral students of color to flourish in fields including media, cyber and technology policy.
Jordan Harrod, MIT Health Sciences and Tech
Jordan Harrod is a doctoral student in the Medical Engineering and Medical Physics (MEMP) program at the Harvard-MIT Health Sciences and Technology (HST) program. The MIT Institute for Medical Engineering and Science (IMES) is HST's home at MIT. Her scholarly work deals with subjects including machine learning, brain-machine interfacing and neuroengineering. She is also an advocate for evidence-based science policy and a science communicator who uses her YouTube channel to educate the public about the role AI plays in modern life.
Muthoni Wanyoike, Nairobi Women Learning and Data Science
Muthoni Wanyoike is an engineer-in-residence at Africa’s Talking, an API platform for software developers in Africa. She has a background in actuarial science and is a self-taught data scientist who is passionate about creating accessible and scalable AI solutions in Africa. She is also the co-founder of the Nairobi Women Learning and Data Science Community and is at the forefront of the African AI scene, ardently working to bring learning opportunities to African women interested in AI.
Ruth Agbakoba, Co-Founder, Black in Data
Ruth Agbakoba is an honorary research fellow at University College London Institute of Health Informatics. Agbakoba holds a doctorate in digital health innovation from the University of Glasgow, Scotland. Her work draws on various interests in healthcare technology to seek breakthroughs in the field. Agbakoba promotes STEM education for young women from underrepresented backgrounds in the United Kingdom. She co-founded Black in Data, an organization dedicated to fostering community, offering professional development opportunities and ultimately elevating the voices of Black people in data science.
Ruha Benjamin, Ida B. Wells Just Data Lab
Ruha Benjamin is professor of African American Studies at Princeton University, founding director of the Ida B. Wells Just Data Lab and author of the award-winning book Race After Technology: Abolitionist Tools for the New Jim Code. Benjamin writes, speaks and teaches about intersections between technology, inequality and health, among other topics.
Yeshimabeit Milner, Data for Black Lives
Yeshimabeit Milner is the co-founder and executive director of Data 4 Black Lives, which aims to use the power of data science to drive change for Black people. The movement employs a range of data science tools and techniques to combat bias in AI and empower communities of color. Milner is an established movement builder and campaign organizer and has received several accolades including an Echoing Green Fellowship and a Forbes 30 Under 30 Social Entrepreneur award.
Timnit Gebru, Ethical AI Team at Google
Timnit Gebru holds a doctorate in artificial intelligence and works as a research scientist and co-lead of Google’s Ethical Artificial Intelligence team. She has conducted research on the ethical and social implications of AI and has built algorithms for a range of well-known tech products, perhaps most notably the iPad. Gebru is an advocate for diversity and inclusion in AI and has actively called for greater transparency from the AI industry as well as new norms and cultural shifts to promote inclusion of underrepresented groups in AI.
Joy Buolamwini, Algorithmic Justice League
Joy Buolamwini is the founder of the Algorithmic Justice League, an organization dedicated to raising public awareness and empowering advocates, communities and other stakeholders to mitigate the effects and harms of AI bias. She has worked to expose the social implications of AI and has written and created art installations to advocate for data protections and justice. Buolamwini has been dubbed one of Forbes’ 2019 World’s Greatest Leaders and a documentary centering her work, Coded Bias, has been hailed by the New York Times as “The most clear-eyed of several recent documentaries about the perils of Big Tech.”
* Originally published here: https://builtin.com/data-science/black-women-data-science