Juba Ziani
Georgia Institute of Technology, GA·Computer Science·Early-career
Machine learning tools now help make life-changing decisions in healthcare, finance and digital services, but they can produce inaccurate or unequal outcomes. Common fixes rest on two assumptions: the data are fixed, and each algorithm can be repaired on its own.
- Fields
Computer Science
Mathematics & Statistics
Social Science
- Topics
algorithmic fairness
fair machine learning
artificial intelligence
- Grants
- 2 known grants, through Apr 2029
What they work on
- 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.
- Design platform approaches for robust data markets that align incentives between data producers and consumers so that the resulting datasets are more representative and reliable.
- Analyze how large-scale effects, such as competition among platforms and interactions among users, influence data quality and the performance of decisions built on that data.
- Use game theory and optimization to model how humans strategically respond to high-stakes machine learning algorithms and determine how this behavior affects algorithmic fairness.
- Develop fairness interventions for multi-stage decision pipelines and analyze feedback loops to understand the long-term effects of algorithmic decisions on fairness.
More about the research
The lab's two projects question both assumptions. They use game theory, optimization, economics and machine learning to study how people, platforms and institutions respond strategically to algorithms and to the data those algorithms collect. They also ask how those responses shape results over time.
Why it matters
Algorithms used in healthcare, finance and digital services depend on data that are often incomplete or unbalanced, and their effects build up over time. This work could help designers of data platforms and decision systems obtain more representative datasets and fairer long-term outcomes. They would do so by using incentives and by accounting for strategic behavior, multi-stage pipelines and feedback loops.
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.
Kirthevasan Kandasamy
University of Wisconsin-Madison, WI·Computer Science·Early-career
Design data-sharing protocols that assign data-collection duties in a balanced way and give out shared data in proportion to each participant's contribution, using game-theoretic analysis.
Xueru Zhang
Ohio State University, OH·Computer Science
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.
The school
Georgia Institute of Technology: 50 labs on PI Pages, $1.5B a year on research, 572 research doctorates a year.
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