The Academic Minute

On Tippie College of Business at the University of Iowa Week: What do Antiques Roadshow and NFTs have in common? Soogand Alavi, assistant professor of marketing, says more than you might think. Soogand Alavi is an assistant professor of marketing in the University of Iowa’s Tippie College of Business. She studies emerging technologies such as […]

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On Tippie College of Business at the University of Iowa Week: How do we reduce biases in AI learning models? Qihang Lin, Henry B. Tippie research fellow and associate professor in the department of business analytics, explores the options. Qihang Lin is Henry B. Tippie Research Fellow and Associate Professor in the Department of Business […]

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On Tippie College of Business at the University of Iowa Week: How do we reduce biases in AI learning models?

Qihang Lin, Henry B. Tippie research fellow and associate professor in the department of business analytics, explores the options.

Faculty Bio:

Qihang Lin is Henry B. Tippie Research Fellow and Associate Professor in the Department of Business Analytics at the University of Iowa’s Tippie College of Business. His research focuses on optimization and machine learning, with recent work addressing fairness in machine learning and decision-making. He received his Ph.D. in Operations Research from Carnegie Mellon University in 2013. Dr. Lin currently serves as the Faculty Director of the Part-Time Master of Business Analytics program.

Transcript:

Machine learning is increasingly used in high-stakes decision-making for things like hiring, lending, and healthcare–but fairness concerns are also growing. Studies show that machine learning models can unintentionally introduce biases and unfairness toward some groups because of algorithmic design, biased sampling, or societal inequalities encoded into the data. A well-known example is Amazon’s AI hiring tool, which systematically downgraded resumes from women for technical jobs because the model had learned from historical hiring data that favored male applicants.

To address this, we’ve developed optimization algorithms that build fairness constraints into model training. Different from the traditional approach that only maximizes a model’s accuracy, our method provides a structured way to balance fairness and accuracy, for example, by enforcing similar predicted positive rates between groups. We’ve realized in some applicationsthat enforcing absolute fairness can significantly degrade a model’s predictive performance. Therefore, in our method, the users can specify the level of fairness they want in their model according to their applications.

At the end of the day, fairness in AI isn’t just a technical issue—it’s a societal one. By improving the way we optimize for fairness, we can build AI systems that are not only accurate but also equitable and trustworthy, ensuring they serve all groups fairly. This work is just one step toward that goal, but it’s an important one. As machine learning continues to shape our world, we believe fairness should be part of the foundation, not an afterthought.

Read More:[The Gazette] – AI needs regulation to avoid discrimination

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On this Student Spotlight during Tippie College of Business at the University of Iowa Week: Should companies use slang on social media? Probably not. Bryce Pyrah, PhD student in marketing, explains why. Bryce Pyrah is a PhD student in marketing at the University of Iowa’s Tippie College of Business, University of Iowa. His research focuses […]

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On Tippie College of Business at the University of Iowa Week: Do you know a co-worker with a side hustle? Jennifer Nahrgang, professor and Henry B. Tippie Excellence Chair of Management and Entrepreneurship, discusses how it might affect them at work. Jennifer D. Nahrgang is Professor and Henry B. Tippie Excellence Chair of Management and […]

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On Tippie College of Business at the University of Iowa Week: How do we fix the loneliness epidemic? Emily Campion, assistant professor of management and entrepreneurship, discusses one place you can foster closeness. Emily Campion is an assistant professor of management and entrepreneurship in the Tippie College of Business at the University of Iowa. Her research […]

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On Harvey Mudd College Week: It takes time to read a lot of information, and what if you need it fast? Xanda Schofield, assistant professor of computer science, looks beyond AI. Xanda Schofield is an Assistant Professor in Computer Science at Harvey Mudd College. She completed her B.S. in Computer Science and Mathematics at Harvey […]

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On Harvey Mudd College Week: To solve big problems, we may need to look small. Albert Dato, associate professor of engineering, explains why. Albert Dato is an Associate Professor of Engineering at Harvey Mudd College (HMC). His undergraduate research program at HMC focuses on the synthesis and applications of advanced materials that can address challenges […]

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On Harvey Mudd College Week: Reducing the cost of capturing energy from the sun could be crucial to our future. Hal Van Ryswyk, John Stauffer professor of chemistry, looks at one way to do so. Hal Van Ryswyk is a materials chemist doing fundamental research on low-cost photovoltaics with undergraduate coworkers at Harvey Mudd College […]

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On Harvey Mudd College Week: What can fish teach us when they’re out of the water? Leah Mendelson, associate professor of engineering, looks into this. Dr. Leah Mendelson holds a B.S. from Olin College (2011) and a M.S. (2013) and Ph.D. (2017) from MIT, all in mechanical engineering. At Harvey Mudd College, Dr. Mendelson teaches […]

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