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Theory of Fair Machine Learning

CORDIS · observation · Publication date unknown

Theory of Fair Machine Learning 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 trade-off between fairness and accuracy using ensemble classifiers with the majority vote, to cancel out not only errors but also biases. I will also develop illegal bias tracing and long-term fairness capturing to comply with anti-subordination lawfully, using learning theory tools including causality and online learning for moral responsibility. The central objective of this propo

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recordType

award

status

SIGNED

region

EU

value

214934.4

unit

EUR