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--- ml:fairness [2021/04/29 10:37] – created jmflanig
+++ ml:fairness [2023/06/15 07:36] (current) – external edit 127.0.0.1
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 ====== 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]]