[论文解读] Improving fairness in machine learning systems: What do industry practitioners need?

Kenneth Holstein, Jennifer Wortman Vaughan+3 more|arXiv (Cornell University)|Dec 13, 2018

Ethics and Social Impacts of AI参考文献 84被引用 53

一句话总结

所提供的文本是一个 SIGCHI 扩展摘要格式示例，并非关于公正性的研究；它描述了 SIGCHI 提交格式指南和 ACM 权利，而非实证研究。

ABSTRACT

The potential for machine learning (ML) systems to amplify social inequities and unfairness is receiving increasing popular and academic attention. A surge of recent work has focused on the development of algorithmic tools to assess and mitigate such unfairness. If these tools are to have a positive impact on industry practice, however, it is crucial that their design be informed by an understanding of real-world needs. Through 35 semi-structured interviews and an anonymous survey of 267 ML practitioners, we conduct the first systematic investigation of commercial product teams' challenges and needs for support in developing fairer ML systems. We identify areas of alignment and disconnect between the challenges faced by industry practitioners and solutions proposed in the fair ML research literature. Based on these findings, we highlight directions for future ML and HCI research that will better address industry practitioners' needs.

研究动机与目标

• 介绍 SIGCHI 提交的格式要求，以确保外观一致、质量高。
• 描述适用于全球 SIGCHI 读者的推荐写作风格与语言指南。
• 提供关于图、表、参考文献及图示放置的详细说明，以帮助作者。
• 向作者解释 ACM 版权、权限和 PDF 生产政策。

提出的方法

• 使用 chi-ext LaTeX 类文件来格式化提交。
• 遵循指定的字体（8.5 点 Verdana，及 sans-serif 的替代字体）和布局规则。
• 遵循模板中示范的图、表、说明文字和边距等指南。
• 确保 PDF 符合 ACM DL 要求，并在提交前使用指定的 Acrobat 版本测试 PDF。

实验结果

主要发现

• 未报告任何实证研究结果；本文件提供用于格式化和提交就绪的指南。
• 作者指南涵盖语言、术语、包容性以及跨文化可读性，适用于 SIGCHI 受众。
• 本文件详细说明所需章节（版权、参考文献、图、表）以及如何将它们构建以用于 ACM Digital Library 提交。