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English(EN) FairBED: A Bayesian Experimental Design Approach to Gathering Fairer Data

FairBED框架旨在为机器学习收集更公平的数据

作者 PulseAugur 编辑部 · [1 个来源] ·

研究人员推出了一种名为FairBED的新型框架，旨在通过修改数据采集过程来提高机器学习的公平性。FairBED不局限于从现有有偏数据中学习公平模型，而是致力于收集本质上更公平的数据集。该方法通过评估数据集在敏感属性方面的信息量来量化数据集的公平性。这种方法构建了贝叶斯实验设计目标，在关于目标变量的信息增益与关于敏感属性的信息增益最小化之间取得平衡，从而在训练模型中实现更好的公平性-准确性权衡。 AI

影响 这项研究通过从源头上解决数据偏差，可能带来更公平的AI系统。

排序理由 该集群包含一篇详细介绍机器学习新方法的论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv stat.ML 阅读 →

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FairBED框架旨在为机器学习收集更公平的数据

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该集群包含一篇详细介绍机器学习新方法的论文。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv stat.ML TIER_1 English(EN) · Tom Rainforth ·

    FairBED：一种用于收集更公平数据的贝叶斯实验设计方法

    Frameworks for ensuring fairness in machine learning typically focus on learning fair models from existing data. But this endeavor is often undermined by biases already present in that data. We therefore look to modify the data acquisition process itself to help gather fairer dat…

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