Welcome to the Pipeline-Aware Fairness Wiki, a categorization of the recent algorithmic fairness and related literature (ICML, ICLR, FAccT, AIES, EAAMO, CHI, and CSCW for 2018-2022) through a pipeline-aware lens.
Hover over each area of the machine learning pipeline below to see sub-steps in each area, and click to see algorithmic harm case studies, measurement, and mitigation techniques corresponding to that part of the pipeline.
For more detail, see questions in the FAQs.
Please create an account in the wiki to view the pages and to also request additions, or become a moderator to make edits to the page yourself!
If you have additional stages of the pipeline you'd like us to add or any other overall changes, let us know here!
