Even though machine learning (ML) pipelines affect an increasing array of stakeholders, there is little work on how input from stakeholders is recorded and incorporated. We propose FeedbackLogs, addenda to existing documentation of ML pipelines, to track the input of multiple stakeholders. Each log records important details about the feedback collection process, the feedback itself, and how the feedback is used to update the ML pipeline. In this paper, we introduce and formalise a process for collecting a FeedbackLog. We also provide concrete use cases where FeedbackLogs can be employed as evidence for algorithmic auditing and as a tool to record updates based on stakeholder feedback.

翻译：尽管机器学习（ML）流水线影响着日益广泛的利益相关者群体，但关于如何记录与整合利益相关者输入的研究仍十分匮乏。我们提出反馈日志（FeedbackLogs），作为现有ML流水线文档的补充附件，用于追踪多方利益相关者的输入。每条日志详细记录反馈收集过程的关键信息、反馈内容本身，以及反馈如何被用于更新ML流水线的具体方式。本文介绍并形式化了一套收集反馈日志的流程，同时提供了具体应用场景：反馈日志可作为算法审计的证据工具，以及基于利益相关者反馈记录流水线更新的实用手段。