From Parity to Preference-based Notions of Fairness in Classification

author: Muhammad Bilal Zafar, Max Planck Institute for Software Systems, Max Planck Institute
published: Dec. 1, 2017,   recorded: August 2017,   views: 20
Categories

Related Open Educational Resources

Related content

Report a problem or upload files

If you have found a problem with this lecture or would like to send us extra material, articles, exercises, etc., please use our ticket system to describe your request and upload the data.
Enter your e-mail into the 'Cc' field, and we will keep you updated with your request's status.
Lecture popularity: You need to login to cast your vote.
  Bibliography

Description

Many notions of fairness in data-driven decision making are inspired by the concept of discrimination in social sciences and law, and focus on ensuring parity (equality) in treatment or outcomes for different social groups. In this paper, we propose preference-based notions of fairness with the goals of avoiding potential ‘reverse-discrimination’ and enabling high decision accuracy. We introduce tractable proxies to design convex boundary-based classifiers that satisfy these new notions of fairness and show on the ProPublica COMPAS dataset that these notions allow for greater decision accuracy than parity-based fairness.

Link this page

Would you like to put a link to this lecture on your homepage?
Go ahead! Copy the HTML snippet !

Write your own review or comment:

make sure you have javascript enabled or clear this field: