Eunhye Ahn, PhD, MSW
University of Wisconsin-Madison
About
I am an Assistant Professor in the Department of Human Development and Family Studies, with a secondary appointment in Consumer Science, at the School of Human Ecology, University of Wisconsin-Madison. I received my PhD in social work from the University of Southern California, my master's degree in social work from Monash University in Australia, and my bachelor's degree in business administration from Yonsei University in South Korea.
Research
My research asks how we can thoughtfully navigate the inequalities being reshaped by AI, and how we can use these same technologies to close equity gaps. I approach AI from both directions, studying it as a social force and deploying it as a research tool, across three interconnected areas.
The Landscape. I study who is being left behind as AI reshapes society, and how this latest wave of technology transforms existing inequalities rather than simply creating new ones.
The Interaction. I examine how humans can remain critically engaged when using AI in high-stakes decisions, neither defaulting to blind trust nor retreating into blanket rejection, so that these tools genuinely support human judgment and wellbeing.
The System. I turn AI and data science into instruments for equity. Using computational methods, I study how children and families move through service systems, where those systems fail them, and how policy discourse shapes the institutions families encounter.
Two principles guide this work. Truly human-centered AI must be designed around people, not the other way around. And decisions about AI must account for the next generation, not just present conditions.
How I Became a Data Scientist
I'm often asked how I became a data scientist without a computer science background. In 2015, I began exploring whether I could contribute to both social work and computer science by learning data science. I participated in the Melbourne Datathon in 2016 and connected with computational social science faculty at Monash University.
During my MSW program, I taught myself introductory data science through online courses on Coursera, MIT OpenCourseWare, and iTunesU — covering probability, linear algebra, R programming, and machine learning. I continued with data science electives during my doctoral training, including computational thinking, informatics, and ML for health sciences.
In 2019, I was accepted into the Data Science for Social Good Fellowship at Imperial College London — the only social work student accepted in the program's seven-year history. That training, combined with years of self-directed learning, became the foundation for applying data science to my qualifying exam and dissertation on ML fairness in child welfare.
Selected Publications
- Ahn, E. (2025). Who is the Human in Human-Centered AI? AI & Society. doi · 한국어 번역
- Ahn, E., Choi, M., Fowler, P., & Song, I. (2025). Artificial Intelligence (AI) Literacy for Social Work: Implications for Core Competencies. Journal of Society for Social Work Research. doi
- Ahn, E., Tejeda, Y., & Yang, Y. (2024). Examining Fairness in Machine Learning Applied to Support Families. Family Relations. doi
- Ahn, E., An, R., Jonson-Reid, M., & Palmer, L. (2024). Leveraging Machine Learning for Effective Child Maltreatment Prevention. Child Abuse & Neglect. doi
- Ahn, E., Gil, Y., & Putnam-Hornstein, E. (2021). Predicting Youth at High Risk of Aging Out of Foster Care Using Machine Learning Methods. Child Abuse & Neglect. doi
Full list on Google Scholar or in my CV.