# Fairness-enhancing interventions in stream classification

## TL;DR

This paper introduces fairness-enhancing interventions for stream classification that modify input data to ensure fairness throughout data streams, addressing evolving data characteristics and maintaining predictive accuracy.

## Contribution

It proposes a novel method of modifying input data in real-time to promote fairness in sequential data classification, unlike traditional batch fairness approaches.

## Key findings

- Achieves low discrimination scores over data streams
- Maintains good predictive performance
- Effective on both real and synthetic data

## Abstract

The wide spread usage of automated data-driven decision support systems has raised a lot of concerns regarding accountability and fairness of the employed models in the absence of human supervision. Existing fairness-aware approaches tackle fairness as a batch learning problem and aim at learning a fair model which can then be applied to future instances of the problem. In many applications, however, the data comes sequentially and its characteristics might evolve with time. In such a setting, it is counter-intuitive to "fix" a (fair) model over the data stream as changes in the data might incur changes in the underlying model therefore, affecting its fairness. In this work, we propose fairness-enhancing interventions that modify the input data so that the outcome of any stream classifier applied to that data will be fair. Experiments on real and synthetic data show that our approach achieves good predictive performance and low discrimination scores over the course of the stream.