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Fairness in AutoML
Tara Sabooni • Yasaman Samadzadeh
sommario
This project investigates the integration of individual fairness metrics into Automated Ma- chine Learning (AutoML) pipelines, with the goal of aligning model selection and evaluation processes with ethical and socially responsible AI practices. Leveraging IBM’s inFairness li- brary, we design a training pipeline that evaluates models not only based on predictive accuracy but also on individual fairness criteria—specifically, the spouse consistency metric. By embed- ding fairness evaluation within the AutoML loop, the project aims to contribute toward the development of more accountable and equitable AI system
