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To overcome the challenge, we propose an iterative framework for choosing decision attributes, or <jats:italic>features<\/jats:italic>, at each level by formulating <jats:italic>feature selection<\/jats:italic> as a series of mixed integer optimization problems. Both fairness and accuracy requirements are encoded as numerical constraints and solved by an off-the-shelf constraint solver. As a result, the trade-off between fairness and accuracy is quantifiable. At a high level, our method can be viewed as a generalization of the entropy-based greedy search techniques such as  and , and existing fair learning techniques such as  and . Our experimental evaluation on six datasets, for which <jats:italic>demographic parity<\/jats:italic> is used as the fairness metric, shows that the method is significantly more effective in reducing bias than other methods while maintaining accuracy. 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