CORDIS Project
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This project focuses on creating fair machine learning algorithms that balance accuracy and fairness. It aims to address biases in training data and improve decision-making processes across various fields such as recruitment and lending.
Designing fair machine learning algorithms is challenging because the training data is often imbalanced and reflects (sometimes subconscious) biases of human annotators, leading to a possible propagation of biases into future decision-making.
Besides, enforcing fairness usually leads to an inevitable deterioration of accuracy due to restrictions on the space of classifiers.
In this project, I will address this challenge by developing oracle bounds of fairness restraints and a Pareto-dominated tr…
KOBENHAVNS UNIVERSITET
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