FeedbackLogs：记录并将利益相关者反馈纳入机器学习流水线
FeedbackLogs: Recording and Incorporating Stakeholder Feedback into Machine Learning Pipelines
AI总结：
本文提出FeedbackLogs，作为ML流水线文档的补充，用于记录和纳入多利益相关者反馈，形式化收集流程，并展示其在算法审计和记录更新中的用例。
摘要：
尽管机器学习（ML）流水线影响着越来越多的利益相关者，但关于如何记录和纳入利益相关者输入的研究却很少。我们提出FeedbackLogs，作为现有ML流水线文档的补充，以追踪多个利益相关者的输入。每条日志记录了关于反馈收集过程、反馈本身以及如何使用反馈更新ML流水线的重要细节。在本文中，我们介绍并形式化了收集FeedbackLog的流程。我们还提供了具体用例，其中FeedbackLogs可作为算法审计的证据，并作为基于利益相关者反馈记录更新的工具。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
