Abstract:

As algorithms increasingly take managerial and governance roles, it is ever more important to build them to be perceived as fair and adopted by people. With this goal, we propose a procedural justice framework in algorithmic decision-making drawing from procedural justice theory, which lays out elements that promote a sense of fairness among users. As a case study, we built an interface that leveraged two key elements of the framework---transparency and outcome control---and evaluated it in the context of goods division. Our interface explained the algorithm's allocative fairness properties (standards clarity) and outcomes through an input-output matrix (outcome explanation), then allowed people to interactively adjust the algorithmic allocations as a group (outcome control). The findings from our within-subjects laboratory study suggest that standards clarity alone did not increase perceived fairness; outcome explanation had mixed effects, increasing or decreasing perceived fairness and reducing algorithmic accountability; and outcome control universally improved perceived fairness by allowing people to realize the inherent limitations of decisions and redistribute the goods to better fit their contexts, and by bringing human elements into final decision-making.

Bio :

Min Kyung Lee is an assistant professor in the School of Information at the University of Texas at Austin. She has been a director of a Human-AI Interaction Lab since 2016. She is affiliated with UT Austin Machine Learning Lab—one of the first NSF funded national AI research institutes, Good Systems—a UT Austin 8-year Grand Challenge to design responsible AI technologies, and Texas Robotics. Previously, she was a research scientist in the Machine Learning Department at Carnegie Mellon University. Dr. Lee has conducted some of the first studies that empirically examine the social implications of algorithms’ emerging roles in management and governance in society. She has extensive expertise in developing theories, methods and tools for human-centered AI and deploying them in practice through collaboration with real-world stakeholders and organizations. She developed a participatory framework that empowers community members to design matching algorithms that govern their own communities. Her current research is inspired by and complements her previous work on social robots for long-term interaction, seamless human-robot handovers, and telepresence robots. Dr. Lee is a Siebel Scholar and has received the Allen Newell Award for Research Excellence, research grants from NSF and Uptake, and eight best paper awards and honorable mentions and two demo/video awards in venues such as CHI, CSCW, DIS, HRI and MobiSys. She is an Associate Editor of Human-Computer Interaction and served as a Senior Associate Editor of ACM Transactions on Human-Robot Interaction from 2017-2023. Her work has been featured in media outlets such as the New York Times, New Scientist, Washington Post, MIT Technology Review and CBS. She received a PhD and a MS in Human-Computer Interaction and an MDes in Interaction Design from Carnegie Mellon University and a BS from KAIST. Dr. Lee is an aspiring explorer. She hiked in 25 national parks in 5 countries, and skied in the U.S., Canada, Italy, France, Switzerland, Japan, and Korea.