23/08/2020

Algorithmic decision making with conditional fairness

Renzhe Xu, Peng Cui, Kun Kuang, Bo Li, Linjun Zhou, Zheyan Shen, Wei Cui

Keywords: conditional independence, fairness

Abstract Paper Similar Papers

Abstract: Nowadays fairness issues have raised great concerns in decision-making systems. Various fairness notions have been proposed to measure the degree to which an algorithm is unfair. In practice, there frequently exist a certain set of variables we term as fair variables, which are pre-decision covariates such as users’ choices. The effects of fair variables are irrelevant in assessing the fairness of the decision support algorithm. We thus define conditional fairness as a more sound fairness metric by conditioning on the fairness variables. Given different prior knowledge of fair variables, we demonstrate that traditional fairness notations, such as demographic parity and equalized odds, are special cases of our conditional fairness notations. Moreover, we propose a Derivable Conditional Fairness Regularizer (DCFR), which can be integrated into any decision-making model, to track the trade-off between precision and fairness of algorithmic decision making. Specifically, an adversarial representation based conditional independence loss is proposed in our DCFR to measure the degree of unfairness. With extensive experiments on three real-world datasets, we demonstrate the advantages of our conditional fairness notation and DCFR.

The video of this talk cannot be embedded. You can watch it here:
https://dl.acm.org/doi/10.1145/3394486.3403263#sec-supp
(Link will open in new window)
 0
 0
 0
 0
  Share    
This is an embedded video. Talk and the respective paper are published at KDD 2020 virtual conference. If you are one of the authors of the paper and want to manage your upload, see the question "My papertalk has been externally embedded..." in the FAQ section.

Comments

Post Comment
no comments yet
code of conduct: tbd Characters remaining: 140

Similar Papers

 0
 0
 0
 0
 15:42 
 0
 0
 0
 0
 8:12 
 0
 0
 0
 0
 15:17 
 0
 0
 0
 0
 9:07 
 0
 0
 0
 0
 16:39 
 0
 0
 0
 0
 19:30 
 0
 0
 0
 0
 13:41 
 0
 0
 0
 0
 11:05 
 0
 0
 0
 0
 14:28 
 0
 0
 0
 0
 13:34 
 0
 0
 0
 0
 14:47 
 0
 0
 0
 0
 5:10 
 0
 0
 0
 0
 15:07 
 0
 0
 0
 0
 3:25 
 0
 0
 0
 0
 3:17 
 0
 0
 0
 0
 15:00 
 0
 0
 0
 0
 3:16 
 0
 0
 0
 0
 15:58 
 0
 0
 0
 0
 3:12 
 0
 0
 0
 0
 4:41 
 0
 0
 0
 0
 15:03 
 0
 0
 0
 0
 17:27 
 0
 0
 0
 0
 17:04 
 0
 0
 0
 0
 3:15 
 0
 0
 0
 0
 5:12 
 0
 0
 0
 0
 3:23 
 0
 0
 0
 0
 3:21 
 0
 0
 0
 0
 4:55 
 0
 0
 0
 0
 14:58 
 0
 0
 0
 0
 3:21 
 0
 0
 0
 0
 6:36 
 0
 0
 0
 0
 14:16 
 0
 0
 0
 0
 15:03 
 0
 0
 0
 0
 19:36 
 0
 0
 0
 0
 2:56 
 0
 0
 0
 0
 3:21 
 0
 0
 0
 0
 18:46 
 0
 0
 0
 0
 14:02 
 0
 0
 0
 1
 5:39 
 0
 0
 0
 0
 12:52