论文标题
基于启发式的自动决策学习弱学习
Heuristic-Based Weak Learning for Automated Decision-Making
论文作者
论文摘要
机器学习系统通常会偶然地影响许多利益相关者和用户群体。先前的研究通过汇总大量手动标记的成对比较来调和用户的偏好，但是该技术可能是昂贵或不切实际的。我们如何降低参与算法设计的障碍？我们建议从专注的受影响用户样本中收集排名的决策启发式方法，而不是为人群创建简化的标签任务。借助来自两种用例的经验数据，我们表明我们的弱学习方法几乎不需要手动标记，它与参与者的成对选择几乎与完全有监督的方法一样。
Machine learning systems impact many stakeholders and groups of users, often disparately. Prior studies have reconciled conflicting user preferences by aggregating a high volume of manually labeled pairwise comparisons, but this technique may be costly or impractical. How can we lower the barrier to participation in algorithm design? Instead of creating a simplified labeling task for a crowd, we suggest collecting ranked decision-making heuristics from a focused sample of affected users. With empirical data from two use cases, we show that our weak learning approach, which requires little to no manual labeling, agrees with participants' pairwise choices nearly as often as fully supervised approaches.
