论文标题
部分可观测时空混沌系统的无模型预测
A Labeling Task Design for Supporting Algorithmic Needs: Facilitating Worker Diversity and Reducing AI Bias
论文作者
论文摘要
有关监督机器学习（ML）的研究建议，来自各种背景的工人参与培训数据集标签以减少算法偏见。此外，对于提高ML性能，进一步使微观任务复杂化的复杂任务是对图像中对象进行分类的复杂任务。这项研究旨在开发一个任务设计，并结合人们的公平参与，无论其特定背景或任务的难度如何。通过与来自来自不同背景的75个标签的合作3个月，我们分析了工人的日志数据和相关叙述，以确定任务的障碍和帮助者。研究结果表明，工人的决策趋势因其背景而异。我们发现，积极帮助工人和机器的反馈意见的社区可以使人们容易从事作品。因此，可以预料的是，ML的偏见可以缓解。基于这些发现，我们建议一种连接标签，机器和社区的扩展人类方法，而不是隔离单个工人。
Studies on supervised machine learning (ML) recommend involving workers from various backgrounds in training dataset labeling to reduce algorithmic bias. Moreover, sophisticated tasks for categorizing objects in images are necessary to improve ML performance, further complicating micro-tasks. This study aims to develop a task design incorporating the fair participation of people, regardless of their specific backgrounds or task's difficulty. By collaborating with 75 labelers from diverse backgrounds for 3 months, we analyzed workers' log-data and relevant narratives to identify the task's hurdles and helpers. The findings revealed that workers' decision-making tendencies varied depending on their backgrounds. We found that the community that positively helps workers and the machine's feedback perceived by workers could make people easily engaged in works. Hence, ML's bias could be expectedly mitigated. Based on these findings, we suggest an extended human-in-the-loop approach that connects labelers, machines, and communities rather than isolating individual workers.
