论文标题
设计用于机器学习偏见的半自动检测的工具：访谈研究
Designing Tools for Semi-Automated Detection of Machine Learning Biases: An Interview Study
论文作者
论文摘要
机器学习模型通常会做出对输入数据的某些亚组偏差的预测。当未被发现时，机器学习偏见可能构成重大的财务和道德意义。涉及人类循环的半自动化工具可以促进偏置检测。然而，对其设计所涉及的考虑知之甚少。在本文中，我们对11位机器学习从业人员进行了一项访谈研究，以调查围绕半自动化偏见检测工具的需求。根据调查结果，我们重点介绍了设计的四个考虑因素，以指导系统设计师，他们旨在为偏见检测创建未来的工具。
Machine learning models often make predictions that bias against certain subgroups of input data. When undetected, machine learning biases can constitute significant financial and ethical implications. Semi-automated tools that involve humans in the loop could facilitate bias detection. Yet, little is known about the considerations involved in their design. In this paper, we report on an interview study with 11 machine learning practitioners for investigating the needs surrounding semi-automated bias detection tools. Based on the findings, we highlight four considerations in designing to guide system designers who aim to create future tools for bias detection.
