Eunhye Ahn, PhD, MSW
University of Wisconsin-Madison
About
I am an Assistant Professor at the School of Human Ecology, University of Wisconsin-Madison. I received my PhD from the University of Southern California and my MSW from Monash University, Australia.
My research asks: how can we thoughtfully navigate inequalities reshaped by AI and close equity gaps? I explore this question across three interconnected areas — the landscape of AI inequality, human interaction with AI, and the systems that serve children and families. My work is guided by two principles: that truly human-centered AI must design around people (not the other way around), and that AI decisions must consider the next generation, not just current conditions.
Research
I address this question in three interconnected areas:
The Landscape — I study who is being left behind as AI reshapes society and how this latest iteration of technology is transforming existing inequalities.
The Interaction — I examine how to ensure humans remain critically engaged when using AI in high-stakes decision-making, rather than defaulting to blind trust or blanket rejection.
The System — I use data science and AI to understand how children and families move through services, where systems fail them, and how policy discourse shapes the institutions families encounter.
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.