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[2L1-GS-11-01] Addressing Bias in Machine Learning Models Using Marginal Contribution
Keywords:Fairness in AI, Game Theory
Fairness in AI is a crucial aspect of modern machine learning.
We focus on the correlation between sensitive and non-sensitive variables which plays a trick when learning models, known as the red-lining effect.In this paper, we present a new algorithm for handling the correlation in machine learning models using marginal contribution. We first clarify a necessarily and sufficient condition between marginal contribution and the independence of sensitive and non-sensitive variables, and then use this condition to develop an algorithm for addressing correlation in models. We then evaluate its performance through empirical experiments.
We focus on the correlation between sensitive and non-sensitive variables which plays a trick when learning models, known as the red-lining effect.In this paper, we present a new algorithm for handling the correlation in machine learning models using marginal contribution. We first clarify a necessarily and sufficient condition between marginal contribution and the independence of sensitive and non-sensitive variables, and then use this condition to develop an algorithm for addressing correlation in models. We then evaluate its performance through empirical experiments.
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