On Discrimination Discovery and Removal in Ranked Data using Causal Graph

03/05/2018
by   Yongkai Wu, et al.
0

Predictive models learned from historical data are widely used to help companies and organizations make decisions. However, they may digitally unfairly treat unwanted groups, raising concerns about fairness and discrimination. In this paper, we study the fairness-aware ranking problem which aims to discover discrimination in ranked datasets and reconstruct the fair ranking. Existing methods in fairness-aware ranking are mainly based on statistical parity that cannot measure the true discriminatory effect since discrimination is causal. On the other hand, existing methods in causal-based anti-discrimination learning focus on classification problems and cannot be directly applied to handle the ranked data. To address these limitations, we propose to map the rank position to a continuous score variable that represents the qualification of the candidates. Then, we build a causal graph that consists of both the discrete profile attributes and the continuous score. The path-specific effect technique is extended to the mixed-variable causal graph to identify both direct and indirect discrimination. The relationship between the path-specific effects for the ranked data and those for the binary decision is theoretically analyzed. Finally, algorithms for discovering and removing discrimination from a ranked dataset are developed. Experiments using the real dataset show the effectiveness of our approaches.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
06/14/2022

Causal Discovery for Fairness

It is crucial to consider the social and ethical consequences of AI and ...
research
11/11/2019

Fairness through Equality of Effort

Fair machine learning is receiving an increasing attention in machine le...
research
10/02/2015

Exposing the Probabilistic Causal Structure of Discrimination

Discrimination discovery from data is an important task aiming at identi...
research
11/15/2021

An Outcome Test of Discrimination for Ranked Lists

This paper extends Becker (1957)'s outcome test of discrimination to set...
research
02/16/2020

Convex Fairness Constrained Model Using Causal Effect Estimators

Recent years have seen much research on fairness in machine learning. He...
research
11/05/2018

FairMod - Making Predictive Models Discrimination Aware

Predictive models such as decision trees and neural networks may produce...
research
09/10/2018

Using Image Fairness Representations in Diversity-Based Re-ranking for Recommendations

The trade-off between relevance and fairness in personalized recommendat...

Please sign up or login with your details

Forgot password? Click here to reset