Pooling of Causal Models under Counterfactual Fairness via Causal Judgement Aggregation

05/24/2018
by   Fabio Massimo Zennaro, et al.
0

In this paper we consider the problem of combining multiple probabilistic causal models, provided by different experts, under the requirement that the aggregated model satisfy the criterion of counterfactual fairness. We build upon the work on causal models and fairness in machine learning, and we express the problem of combining multiple models within the framework of opinion pooling. We propose two simple algorithms, grounded in the theory of counterfactual fairness and causal judgment aggregation, that are guaranteed to generate aggregated probabilistic causal models respecting the criterion of fairness, and we compare their behaviors on a toy case study.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
10/01/2018

Counterfactually Fair Prediction Using Multiple Causal Models

In this paper we study the problem of making predictions using multiple ...
research
10/03/2021

Enhancing Model Robustness and Fairness with Causality: A Regularization Approach

Recent work has raised concerns on the risk of spurious correlations and...
research
08/30/2021

Transport-based Counterfactual Models

Counterfactual frameworks have grown popular in explainable and fair mac...
research
07/20/2023

Mitigating Voter Attribute Bias for Fair Opinion Aggregation

The aggregation of multiple opinions plays a crucial role in decision-ma...
research
02/28/2022

Selection, Ignorability and Challenges With Causal Fairness

In this paper we look at popular fairness methods that use causal counte...
research
09/18/2019

Causal Modeling for Fairness in Dynamical Systems

In this work, we present causal directed acyclic graphs (DAGs) as a unif...
research
08/04/2021

Under the Radar – Auditing Fairness in ML for Humanitarian Mapping

Humanitarian mapping from space with machine learning helps policy-maker...

Please sign up or login with your details

Forgot password? Click here to reset