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.

我们提出了 FeedbackLogs，这是现有机器学习（ML）流程文档的附加部分，用于跟踪多个利益相关者的输入。每个记录都包括有关反馈收集过程、反馈本身以及如何使用反馈来更新 ML 流程的重要细节。我们在本文中引入并规范了收集 FeedbackLog 的过程，并提供了具体的用例，其中 FeedbackLogs 可以作为算法审计的证据和记录基于利益相关者反馈的更新的工具。