Fairness in machine learning (ML) applications is an important practice for developers in research and industry. In ML applications, unfairness is triggered due to bias in the data, curation process, erroneous assumptions, and implicit bias rendered within the algorithmic development process. As ML applications come into broader use developing fair ML applications is critical. Literature suggests multiple views on how fairness in ML is described from the users perspective and students as future developers. In particular, ML developers have not been the focus of research relating to perceived fairness. This paper reports on a pilot investigation of ML developers perception of fairness. In describing the perception of fairness, the paper performs an exploratory pilot study to assess the attributes of this construct using a systematic focus group of developers. In the focus group, we asked participants to discuss three questions- 1) What are the characteristics of fairness in ML? 2) What factors influence developers belief about the fairness of ML? and 3) What practices and tools are utilized for fairness in ML development? The findings of this exploratory work from the focus group show that to assess fairness developers generally focus on the overall ML application design and development, i.e., business-specific requirements, data collection, pre-processing, in-processing, and post-processing. Thus, we conclude that the procedural aspects of organizational justice theory can explain developers perception of fairness. The findings of this study can be utilized further to assist development teams in integrating fairness in the ML application development lifecycle. It will also motivate ML developers and organizations to develop best practices for assessing the fairness of ML-based applications.

翻译：机器学习（ML）应用中的公平性，对于研究及产业界的开发者而言是一项重要实践。在ML应用中，不公平性源于数据偏差、数据整理过程、错误假设以及算法开发过程中隐含的偏见。随着ML应用日益广泛，开发公平的ML应用变得至关重要。文献从用户及未来开发者（学生）的角度，提出了多种描述ML公平性的观点。值得注意的是，ML开发者本身并非感知公平性相关研究的重点。本文报告了一项关于ML开发者公平性感知的初步调查。为描述公平性感知，本文开展了一项探索性试点研究，通过系统性开发者焦点小组评估该构念的属性。在焦点小组中，我们邀请参与者讨论三个问题：1）ML中公平性的特征是什么？2）哪些因素影响开发者对ML公平性的信念？3）在ML开发中，有哪些实践和工具用于实现公平性？焦点小组探索性工作的结果表明，为评估公平性，开发者通常关注ML应用的整体设计与开发，即业务特定需求、数据收集、预处理、处理中及后处理环节。因此，我们得出结论：组织公平理论中的程序性维度能够解释开发者的公平性感知。本研究的发现可进一步用于协助开发团队将公平性融入ML应用开发生命周期，并将激励ML开发者及组织制定评估基于ML应用公平性的最佳实践。