While algorithmic fairness is a thriving area of research, in practice,
mitigating issues of bias often gets reduced to enforcing an arbitrarily chosen
fairness metric, either by enforcing fairness constraints during the
optimization step, post-processing model outputs, or by manipulating the
training data. Recent work has called on the ML community to take a more
holistic approach to tackle fairness issues by systematically investigating the
many design choices made through the ML pipeline, and identifying interventions
that target the issue's root cause, as opposed to its symptoms. While we share
the conviction that this pipeline-based approach is the most appropriate for
combating algorithmic unfairness on the ground, we believe there are currently
very few methods of \emph{operationalizing} this approach in practice. Drawing
on our experience as educators and practitioners, we first demonstrate that
without clear guidelines and toolkits, even individuals with specialized ML
knowledge find it challenging to hypothesize how various design choices
influence model behavior. We then consult the fair-ML literature to understand
the progress to date toward operationalizing the pipeline-aware approach: we
systematically collect and organize the prior work that attempts to detect,
measure, and mitigate various sources of unfairness through the ML pipeline. We
utilize this extensive categorization of previous contributions to sketch a
research agenda for the community. We hope this work serves as the stepping
stone toward a more comprehensive set of resources for ML researchers,
practitioners, and students interested in exploring, designing, and testing
pipeline-oriented approaches to algorithmic fairness.

通过调查公平机器学习文献并收集先前工作，本研究为机器学习研究者、从业者和学生绘制了探索、设计和测试面向算法公平性的管道导向方法的更全面资源集。