@InProceedings{blum_et_al:LIPIcs.FORC.2020.3, author = {Blum, Avrim and Stangl, Kevin}, title = {{Recovering from Biased Data: Can Fairness Constraints Improve Accuracy?}}, booktitle = {1st Symposium on Foundations of Responsible Computing (FORC 2020)}, pages = {3:1--3:20}, series = {Leibniz International Proceedings in Informatics (LIPIcs)}, ISBN = {978-3-95977-142-9}, ISSN = {1868-8969}, year = {2020}, volume = {156}, editor = {Roth, Aaron}, publisher = {Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik}, address = {Dagstuhl, Germany}, URL = {https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.FORC.2020.3}, URN = {urn:nbn:de:0030-drops-120192}, doi = {10.4230/LIPIcs.FORC.2020.3}, annote = {Keywords: fairness in machine learning, equal opportunity, bias, machine learning} }
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