CMU Ph.D. student presents research on AI bias and fairness at international conference

Central Michigan University doctoral student Hairu Fan is advancing research into responsible artificial intelligence, presenting two studies examining AI bias and fairness at an international research conference in September.

Fan, a Ph.D. student in CMU’s Department of Statistics, Actuarial and Data Sciences, presented her work at the IEEE 4th International Conference on Artificial Intelligence, Blockchain, and Internet of Things (AIBThings 2026), held Sept. 5–6. Her two studies focus on how disparities can emerge in AI-assisted occupation classification and how textual information and model attribution methods may help explain differences in AI outcomes.

The projects grew out of independent research Fan pursued over the summer and reflect her interest in making AI systems more responsible, transparent and trustworthy.

One study, “Hybrid Bias Distortion in AI-Assisted Occupation Classification: A Pilot Study,” examines potential disparities in AI-assisted occupation classification. The second, “From Attribution to Disparity: A Textual Pathway Analysis of Bias in BERT-Based Occupation Classification,” investigates how textual signals and model attribution pathways may contribute to differences in classification outcomes.

Together, the studies look beyond whether an AI system produces biased results to explore how those disparities may develop and what researchers can learn from the process.

“For me, identifying a disparity is only the beginning,” Fan said. “I’m increasingly interested in understanding how that disparity develops, what evidence helps explain it, and how we can use that information to make AI systems more responsible and trustworthy.”

Fan collaborated with co-author Shiyuan Wang on the projects and welcomed the opportunity to share her findings and exchange ideas with other researchers at AIBThings. The experience also reinforced her interest in pursuing research that brings together statistical methods, computational tools and questions about the real-world impact of emerging technologies.

As she continues her doctoral studies at CMU, Fan plans to further investigate statistical and computational approaches to fairness, bias and decision-making in AI systems.

She credited support from CMU’s Office of Research and Graduate Studies and the Department of Statistics, Actuarial and Data Sciences with helping make her conference participation possible. She also recognized the guidance of Dr. Ahmed Abdelgawad in CMU’s School of Engineering and Technology, whose encouragement and willingness to share advice have contributed to her academic development.

“I’m grateful to the Office of Research and Graduate Studies and the Department of Statistics, Actuarial and Data Sciences for supporting my participation in AIBThings 2026,” Fan said. “I’m also especially thankful to Dr. Ahmed Abdelgawad in the School of Engineering and Technology for his continued guidance and encouragement. He has always been willing to answer my questions and share advice, and he has played an important role in my academic development.”

For Fan, research offers an opportunity to investigate questions that matter to her while contributing to a better understanding of technologies increasingly used in decision-making.

“Research gives me the opportunity to keep asking questions that I care deeply about,” she said. “Responsible AI is an area I hope to continue exploring throughout my doctoral studies, particularly where statistical thinking can help us better understand how AI systems affect people and decision-making.”