The Center for Gender, Sexualities and Women's Studies Research at the University of Florida offers an interdisciplinary forum for the study of gender and sexualities, their intersections with race/ethnicity, class, and other sociocultural systems and their functions in cultures and societies. Part of the mission of the Center, is to stimulate individual and collaborative research by UF faculty engaged in the study of women, gender and sexualities.
As part of the University of Florida's Artificial Intelligence (AI) Initiative, the Center for Gender, Sexualities and Women's Studies Research is hiring faculty with an AI background. To learn more about the Center’s work in the AI field, we interviewed Bonnie Moradi, professor of psychology and director of the Center for Gender, Sexualities, and Women’s Studies Research.
What opportunities have opened in the Center's field of studies thanks to the emergence of artificial intelligence?
A core aspect of research and teaching in our Center is uncovering and addressing bias and social inequalities along gender, race, sexuality and other axes of power. Applying these tools to examining bias in AI will be an important area of expansion for our Center and of critical importance to the development and use of AI.
A 2020 state of data science survey identified "social impacts from bias” as the No. 1 ranked “biggest issue to tackle in AI.” Government agencies like the FTC have warned companies that big data analytics can promote bias and harm to consumers and have provided guidance on mitigating bias in Using Artificial Intelligence and Algorithms. These are major social issues and opportunities that our discipline can address. Intersectional feminist and critical race analysis are core aspects of gender, sexuality and women's studies as a discipline, and central to our work in the Center. These theoretical and methodological underpinnings are critical to addressing social inequalities in AI and data science and in using AI and data science to advance social justice. With our AI position, we hope to help create less biased and more feminist, anti-racist and socially just AI tools and applications.
How do you see your curriculum evolving as you expand AI research and opportunities within the Center?
We will offer courses that explore how gender, race, sexuality and other systems of power can become reified through the use of AI, how these biases can be detected and mitigated, and how AI and data science can be used as tools for social justice. We plan to build on our existing strengths in offering classes on feminist research methods and social justice praxis to offer classes such as Gender, Race, and Technoscience; Feminist Data Science and AI and Data Science for Social Justice. These classes will serve students across campus and contribute to innovative curricula on AI, data science and social justice.
The Center for Gender, Sexualities and Women's Studies Research is currently hiring faculty with an AI background. How do you envision these faculty will help advance the Center's field of studies through artificial intelligence?
Our position is focused on hiring a faculty member in feminist technoscience and related fields who specializes in critical analysis and applications of AI and data science. This includes scholars who focus on how AI and data science can shape and be shaped by gender, race, sexuality and other social inequalities as well as uses of AI and data science to promote ethical accountability and social justice. This new colleague will be able to collaborate with scholars working in AI bias, ethics, and equity across the social sciences, humanities, natural sciences, law, computer science and engineering, and other disciplines. We also anticipate that this new colleague will be an asset in collaborating with all researchers using the UF AI resources in projects aimed to theorize, analyze and mitigate bias in the development and applications of AI algorithms, machine learning, big data and more.
In your field, what kinds of pressing issues do you think AI faculty research will be able to address?
There is significant concern in AI and data science about bias, equity and social justice. As one concrete example, facial recognition algorithms are becoming ubiquitous in policing and surveillance (e.g., security videos, mugshot databases) and in access to spaces (e.g., schools, airports) and consumer products (e.g., phones, computers). MIT's Gender Shades project analyzed the gender classification accuracy of top facial recognition algorithms and revealed 8%-21% more error for women, 12%-19% more error for people of color, and gender x race analysis revealed 21%-34% more error for women of color. As this example illustrates, AI can magnify social inequalities. However, with the tools of intersectional feminist and critical race theory and analysis, it can also help to mitigate them. These are the pressing issues our position is focused on.
What steps are you taking to build a cohort of AI researchers that represents our diverse society?
Our position and vision are inspired by the amazing work of organizations like Algorithmic Justice League, Feminist AI, Black in AI and work on data feminism. We have disseminated information about our position to these and many more organizations that focus on advancing feminist, anti-racist, and social justice focused AI and data science. We have also disseminated our position to a broad network of scholars in AI and data science who are, for example, people of color, women and sexual minority people.
What opportunities does AI present to the Center for collaboration across disciplines, colleges or units?
Crossing disciplines and serving students and faculty across campus is core to our unit's mission and identity. In addition to our 500+ majors, minors, MA students, and graduate certificate students, we serve students across campus through our course offerings, programs and events. In addition to our core faculty, we have 100+ affiliate faculty from all colleges on campus. We developed our position in collaboration with other units and with a vision for cross-disciplinary collaborations that advance equity and social justice in AI and data science. Many UF students and faculty are concerned about the potential harm of bias in AI. We want UF to be at the forefront of generating AI research and teaching that has at its core a commitment to not only avoid and mitigate bias, but to advance more equitable and just outcomes across health, business, media, government, law and more by using AI and data science as social justice tools.
How does the culture of the Center foster an environment where new faculty members can excel in AI?
Our Center faculty, students and staff, and our broad network of campus and community partners take seriously the responsibility of welcoming and supporting new faculty. Our commitment to helping new faculty make connections across campus starts in the recruitment process where we include meetings with potential collaborators and support organizations for each candidate. Once our new colleague is here, we establish cross-disciplinary mentoring committees that include core and affiliate faculty to support and advise them toward successful tenure and promotion. We offer grant and fellowship opportunities through the Center. Most importantly, we have an extensive network of student, faculty, and community partners who are eager and committed to welcoming and supporting the success of our new colleague.
Professor of Psychology
Director, Center for Gender, Sexualities, and Women's Studies Research