Xueru Zhang
Ohio State University, OH·Computer Science
Algorithms increasingly help decide who gets hired, receives a loan or is admitted to college. People are not passive in these decisions; they change their behavior in response to them.
- Fields
Computer Science
Engineering
Social Science
- Topics
algorithmic fairness
fair machine learning
strategic classification
- Grants
- 2 known grants, through Sep 2027
What they work on
- Build a mathematical model of how individuals strategically and repeatedly adapt to machine learning decision systems, and how this feeds back into model training. The model combines game theory and stochastic control.
- Validate the human behavioral model and use the resulting framework to design interventions that improve long-term welfare and equity in settings such as lending and hiring.
- Develop machine learning methods that detect construction workers' physical fatigue, mental stress and heat stress from wearable sensor and environmental data. The methods should cope with variation between people and preserve privacy.
- Create a jobsite digital twin, a set of shared health and safety maps, with automated feedback to workers and managers, based on data from a new flexible wearable sensor.
More about the research
The research studies how machine learning systems and people affect each other over time. It also looks at how to measure workers' health with sensors and AI. In both projects, the challenge is to build learning systems that stay reliable, fair and private when used on real people.
Why it matters
Better models of how people respond to algorithms could help designers and policymakers keep automated hiring, lending and admissions decisions fair over time, not only at launch. Wearable, privacy-aware health monitoring could help protect nearly 7 million U.S. construction workers from fatigue, stress and heat. Similar systems could serve manufacturing, firefighting and agriculture.
Known grants
From NIH and NSF research grants. Labs often have other funding we can't see.
Closest labs
Xueru Zhang
Ohio State University, OH·Computer Science
Model how individuals strategically and repeatedly change their behavior in response to fair machine learning decisions, using a sequential framework that combines stochastic control and game theory.
Juba Ziani
Georgia Institute of Technology, GA·Computer Science·Early-career
Model how individual data providers shape the outcomes of machine learning systems through strategic choices about what data to share, combining tools from computer science and economics.
Himabindu Lakkaraju
Harvard University, MA·Computer Science·Early-career
Build theoretical and empirical frameworks that measure how faithfully leading explanation methods capture the behavior of different model classes, including linear and non-linear machine learning models.
The school
Ohio State University: 71 labs on PI Pages, $1.6B a year on research, 882 research doctorates a year.
Reach out
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