Fair classification via domain adaptation: a dual adversarial learning approach

HIGHLIGHTS

• who: Yueqing Liang from the Iowa State University, United States have published the research: Fair classification via domain adaptation: A dual adversarial learning approach, in the Journal: (JOURNAL)
• what: The authors propose a dual-adversarial learning framework to learn to adapt sensitive attributes for fair classification in the target domain. In essence, the authors investigate the following challenges: how to adapt the sensitive attributes to the target domain by transferring knowledge from the source domain; and_(2) how to predict the labels accurately 2 and satisfy fairness criteria. The authors focus on group fairness. The . . .
  

   

  

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