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Conformal prediction, fairness and calibration
The internship focuses on the intersection of mathematics, machine learning, and ethical AI, specifically within the domains of conformal prediction, fairness, and calibration. Conformal prediction is a statistical framework that provides mathematically rigorous confidence measures for machine learning predictions, ensuring that the uncertainty quantification is valid under minimal assumptions. In this project, the goal is to explore how conformal prediction methods can be extended or adapted to meet fairness criteria, addressing biases that may arise in datasets or prediction algorithms.
Faculty Supervisor:
Masoud Asgharian;Arthur Charpentier
Student:
Partner:
Layer 6 AI
Discipline:
Mathematics
Sector:
Finance and Insurance; Professional, scientific and technical services
University:
McGill University
Program:
Accelerate
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