FeedbackLogs: Recording and Incorporating Stakeholder Feedback into
  Machine Learning Pipelines
Matthew Barker Emma Kallina Dhananjay Ashok Katherine M. Collins Ashley Casovan Adrian Weller Ameet Talwalkar Valerie Chen Umang Bhatt
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.