Fair Data Adaptation with Quantile Preservation
Fair Data Adaptation with Quantile Preservation
复制标题
具有分位数保留的公平数据适应
DOI：
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复制
发表时间：
2019
期刊：
影响因子：
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通讯作者：
N. Meinshausen
中科院分区：
文献类型：
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作者：
Drago Plečko;N. Meinshausen
分类和回归的公平性最近受到了广泛的关注，并提出了各种部分不相容的标准。可以针对给定的分类器强制执行公平性标准，或者可选地，可以调整数据以确保在数据上训练的每个分类器将遵守期望的公平性标准。提出了一种基于分位数保持的因果结构方程模型数据自适应方法。数据自适应基于数据的假定反事实模型。虽然反事实模型本身无法通过实验验证，但我们表明，即使反事实模型被错误指定，某些人口公平概念仍然得到保证。实现非因果公平概念的确切性质（如人口均等、分离或充分性）取决于基本因果模型的结构和解决变量的选择。我们描述了一种基于随机森林的数据自适应过程的实现，并展示了其在模拟和真实数据上的实际应用。
Fairness of classification and regression has received much attention recently and various, partially non-compatible, criteria have been proposed. The fairness criteria can be enforced for a given classifier or, alternatively, the data can be adapated to ensure that every classifier trained on the data will adhere to desired fairness criteria. We present a practical data adaption method based on quantile preservation in causal structural equation models. The data adaptation is based on a presumed counterfactual model for the data. While the counterfactual model itself cannot be verified experimentally, we show that certain population notions of fairness are still guaranteed even if the counterfactual model is misspecified. The precise nature of the fulfilled non-causal fairness notion (such as demographic parity, separation or sufficiency) depends on the structure of the underlying causal model and the choice of resolving variables. We describe an implementation of the proposed data adaptation procedure based on Random Forests and demonstrate its practical use on simulated and real-world data.
DOI：
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发表时间：
2019
期刊：
In Proceedings of ACM FAT*
影响因子：
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作者：
Milli, Smitha;Miller, John;Dragan, Anca;Hardt, Moritz
通讯作者：
Hardt, Moritz