# On the Impact of Data Quality on Image Classification Fairness

Source: [On the Impact of Data Quality on Image Classification Fairness](<https://www.sota2.com/research/paper/68fa6f2d1e209131cea44440>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-26T01:15:37.285Z

Authors: Aki Barry, Lei Han, Gianluca Demartini

Published: 2023-05-02T16:54:23.000Z

## Abstract

With the proliferation of algorithmic decision-making, increased scrutiny has been placed on these systems. This paper explores the relationship between the quality of the training data and the overall fairness of the models trained with such data in the context of supervised classification. We measure key fairness metrics across a range of algorithms over multiple image classification datasets that have a varying level of noise in both the labels and the training data itself. We describe noise in the labels as inaccuracies in the labelling of the data in the training set and noise in the data as distortions in the data, also in the training set. By adding noise to the original datasets, we can explore the relationship between the quality of the training data and the fairness of the output of the models trained on that data.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2305.01595>)

[PDF](<https://arxiv.org/pdf/2305.01595.pdf>)