Causal Interventions for Fairness

by   Matt J. Kusner, et al.

Most approaches in algorithmic fairness constrain machine learning methods so the resulting predictions satisfy one of several intuitive notions of fairness. While this may help private companies comply with non-discrimination laws or avoid negative publicity, we believe it is often too little, too late. By the time the training data is collected, individuals in disadvantaged groups have already suffered from discrimination and lost opportunities due to factors out of their control. In the present work we focus instead on interventions such as a new public policy, and in particular, how to maximize their positive effects while improving the fairness of the overall system. We use causal methods to model the effects of interventions, allowing for potential interference--each individual's outcome may depend on who else receives the intervention. We demonstrate this with an example of allocating a budget of teaching resources using a dataset of schools in New York City.


page 5

page 8


Data Management for Causal Algorithmic Fairness

Fairness is increasingly recognized as a critical component of machine l...

On the Fairness of Causal Algorithmic Recourse

While many recent works have studied the problem of algorithmic fairness...

What's Sex Got To Do With Machine Learning

Debate about fairness in machine learning has largely centered around co...

What's Sex Got To Do With Fair Machine Learning?

Debate about fairness in machine learning has largely centered around co...

FairPrep: Promoting Data to a First-Class Citizen in Studies on Fairness-Enhancing Interventions

The importance of incorporating ethics and legal compliance into machine...

Causal Scene BERT: Improving object detection by searching for challenging groups of data

Modern computer vision applications rely on learning-based perception mo...

Case Study: Predictive Fairness to Reduce Misdemeanor Recidivism Through Social Service Interventions

The criminal justice system is currently ill-equipped to improve outcome...

Please sign up or login with your details

Forgot password? Click here to reset