Towards Responsible Data-driven Decision Making in Score-Based Systems
Towards Responsible Data-driven Decision Making in Score-Based Systems
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发表时间：
2019
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通讯作者：
Abolfazl Asudeh;H. V. Jagadish;Julia Stoyanovich
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作者：
Abolfazl Asudeh;H. V. Jagadish;Julia Stoyanovich
人类决策者通常会从数据驱动的算法系统中获得帮助，以评估产品，服务或个人等项目的质量，可以通过ML模型学到的过程组合来获得不同的分数。使用人类专家设计的体重向量，其过去的经验和构成项目质量的注释。责任。我们提出技术方法（i），以帮助人类专家设计基于分数的公平和稳定的排名，以及（ii）评估和（如果需要）增强培训数据集的覆盖范围，以进行机器学习任务，例如分类。
Human decision makers often receive assistance from data-driven algorithmic systems that provide a score for evaluating the quality of items such as products, services, or individuals. These scores can be obtained by combining different features either through a process learned by ML models, or using a weight vector designed by human experts, with their past experience and notions of what constitutes item quality. The scores can be used for different evaluation purposes such as ranking or classification. In this paper, we view the design of these scores through the lens of responsibility. We present technical methods (i) to assist human experts in designing fair and stable score-based rankings and (ii) to assess and (if needed) enhance the coverage of a training dataset for machine learning tasks such as classification.