We propose a model-agnostic approach for mitigating the prediction bias of a black-box decision-maker, and in particular, a human decision-maker. Our method detects in the feature space where the black-box decision-maker is biased and replaces it with a few short decision rules, acting as a "fair surrogate". The rule-based surrogate model is trained under two objectives, predictive performance and fairness. Our model focuses on a setting that is common in practice but distinct from other literature on fairness. We only have black-box access to the model, and only a limited set of true labels can be queried under a budget constraint. We formulate a multi-objective optimization for building a surrogate model, where we simultaneously optimize for both predictive performance and bias. To train the model, we propose a novel training algorithm that combines a nondominated sorting genetic algorithm with active learning. We test our model on public datasets where we simulate various biased "black-box" classifiers (decision-makers) and apply our approach for interpretable augmented fairness.

中文翻译：

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增强公平性：增强决策者公平性的可解释模型

我们提出了一种与模型无关的方法来减轻黑盒决策者，特别是人类决策者的预测偏差。我们的方法在特征空间中检测黑盒决策者有偏见的地方，并用一些简短的决策规则替换它，充当“公平代理”。基于规则的代理模型在两个目标下进行训练，预测性能和公平性。我们的模型侧重于实践中常见但与其他公平性文献不同的设置。我们只能对模型进行黑盒访问，并且在预算约束下只能查询一组有限的真实标签。我们为构建代理模型制定了多目标优化方案，同时针对预测性能和偏差进行优化。为了训练模型，我们提出了一种新的训练算法，它将非支配排序遗传算法与主动学习相结合。我们在公共数据集上测试我们的模型，我们模拟各种有偏见的“黑盒”分类器（决策者），并将我们的方法应用于可解释的增强公平性。