NLM Science, Technology, and Society Lecture | Confronting Race, Gender, & Ability Bias in Tech
Date: Wednesday, March 06, 2024
Time: 2:00 p.m. to 3:00 p.m. ET
Type of event: Science, Technology, and Society Lecture
Location: virtual via NIH Videocast or in-person at Building 45, Balcony A (Natcher Conference Center)
More Than a Glitch: Confronting Race, Gender, and Ability Bias in Tech (MIT Press, 2023), as well as the award-winning 2018 book Artificial Unintelligence: How Computers Misunderstand the World. Her research focuses on artificial intelligence in investigative reporting, with particular interests in AI ethics and using data analysis for social good. She appears in the Emmy- nominated documentary “Coded Bias,” now streaming on Netflix. Her work has been supported by the Rockefeller Foundation, the Institute of Museum & Library Services, and the Tow Center at Columbia Journalism School. A former features editor at the Philadelphia Inquirer, she has also worked as a software developer at AT&T Bell Labs and the MIT Media Lab. Her features and essays have appeared in The New York Times, The Atlantic, Slate, Vox, and other outlets. Follow her on X @merbroussard or contact her via meredithbroussard.com.
Sponsored by the NLM Office of Strategic Initiatives, the Science, Technology, and Society Lecture aims to raise awareness around the societal and ethical implications of biomedical research conduct and the use of advanced technologies. Each spring, NLM invites a leading voice working at the intersection of biomedicine, data science, ethics, and justice to present their research and how it relates to the mission and vision of both NLM and NIH. NLM sees such considerations as fundamental to advancing biomedical discovery and human health for the benefit of all.
What if racism, sexism, and ableism aren't just glitches in mostly functional machinery—what if they're coded into our technological systems? In this talk, data scientist and journalist Meredith Broussard explores why neutrality in tech is a myth and how algorithms can be held accountable.
Broussard, one of the few Black female researchers in artificial intelligence, explores a range of examples: from facial recognition technology trained only to recognize lighter skin tones, to mortgage-approval algorithms that encourage discriminatory lending, to the dangerous feedback loops that arise when medical diagnostic algorithms are trained on insufficiently diverse data. Even when such technologies are designed with good intentions, Broussard shows, fallible humans develop programs that can result in devastating consequences.
Broussard argues that the solution isn't to make omnipresent tech more inclusive, but to root out the algorithms that target certain demographics as “other” to begin with. She explores practical strategies to detect when technology reinforces inequality, and offers ideas for redesigning our systems to create a more equitable world.