DeepAI AI Chat
Log In Sign Up

Fairness constraint in Structural Econometrics and Application to fair estimation using Instrumental Variables

by   Samuele Centorrino, et al.
Toulouse School of Economics
Université de Toulouse

A supervised machine learning algorithm determines a model from a learning sample that will be used to predict new observations. To this end, it aggregates individual characteristics of the observations of the learning sample. But this information aggregation does not consider any potential selection on unobservables and any status-quo biases which may be contained in the training sample. The latter bias has raised concerns around the so-called fairness of machine learning algorithms, especially towards disadvantaged groups. In this chapter, we review the issue of fairness in machine learning through the lenses of structural econometrics models in which the unknown index is the solution of a functional equation and issues of endogeneity are explicitly accounted for. We model fairness as a linear operator whose null space contains the set of strictly fair indexes. A fair solution is obtained by projecting the unconstrained index into the null space of this operator or by directly finding the closest solution of the functional equation into this null space. We also acknowledge that policymakers may incur a cost when moving away from the status quo. Achieving approximate fairness is obtained by introducing a fairness penalty in the learning procedure and balancing more or less heavily the influence between the status quo and a full fair solution.


page 1

page 2

page 3

page 4


On Adversarial Bias and the Robustness of Fair Machine Learning

Optimizing prediction accuracy can come at the expense of fairness. Towa...

A Framework for Fairness: A Systematic Review of Existing Fair AI Solutions

In a world of daily emerging scientific inquisition and discovery, the p...

On the Privacy Risks of Algorithmic Fairness

Algorithmic fairness and privacy are essential elements of trustworthy m...

Mitigating Group Bias in Federated Learning: Beyond Local Fairness

The issue of group fairness in machine learning models, where certain su...

DECAF: Generating Fair Synthetic Data Using Causally-Aware Generative Networks

Machine learning models have been criticized for reflecting unfair biase...

Noise-tolerant fair classification

Fair machine learning concerns the analysis and design of learning algor...

Fairness without Regret

A popular approach of achieving fairness in optimization problems is by ...