Cite
fairlib: A Unified Framework for Assessing and Improving Classification Fairness
MLA
Han, Xudong, et al. Fairlib: A Unified Framework for Assessing and Improving Classification Fairness. 2022. EBSCOhost, widgets.ebscohost.com/prod/customlink/proxify/proxify.php?count=1&encode=0&proxy=&find_1=&replace_1=&target=https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&scope=site&db=edsarx&AN=edsarx.2205.01876&authtype=sso&custid=ns315887.
APA
Han, X., Shen, A., Li, Y., Frermann, L., Baldwin, T., & Cohn, T. (2022). fairlib: A Unified Framework for Assessing and Improving Classification Fairness.
Chicago
Han, Xudong, Aili Shen, Yitong Li, Lea Frermann, Timothy Baldwin, and Trevor Cohn. 2022. “Fairlib: A Unified Framework for Assessing and Improving Classification Fairness.” http://widgets.ebscohost.com/prod/customlink/proxify/proxify.php?count=1&encode=0&proxy=&find_1=&replace_1=&target=https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&scope=site&db=edsarx&AN=edsarx.2205.01876&authtype=sso&custid=ns315887.