====== Fairness ======
See related work on fairness in [[https://arxiv.org/pdf/2007.13982.pdf|Duchi 2020]].

  * [[https://arxiv.org/pdf/1104.3913.pdf|Dwork, et al 2011 - Fairness Through Awareness]]
  * [[https://arxiv.org/pdf/1610.02413.pdf|Hardt et al 2016 - Equality of Opportunity in Supervised Learning]]  "We show how to optimally adjust any learned predictor so as to remove discrimination according to our definition."
  * [[https://arxiv.org/pdf/1706.02744.pdf|Kilbertus et al 2017 - Avoiding Discrimination through Causal Reasoning]]
  * [[https://arxiv.org/pdf/1711.05144.pdf|Kearns et al 2017 - Preventing Fairness Gerrymandering: Auditing and Learning for Subgroup Fairness]]
  * [[https://www.microsoft.com/en-us/research/uploads/prod/2020/05/Fairlearn_WhitePaper-2020-09-22.pdf|Bird et al 2020 - Fairlearn: A Toolkit for Assessing and Improving Fairness in AI]]

===== Classes and Tutorials =====
  * [[http://web.cs.ucla.edu/~kwchang/talks/emnlp19-fairnlp/|Tutorial: Bias and Fairness in Natural Language Processing]]

===== Related Pages =====
  * [[nlp:Bias]]
  * [[Distribution Shift]] (Methods that are not robust to distribution shift may not be fair across populations)
  * [[nlp:Ethics]]