Despite the high interest for Machine Learning (ML) in academia and industry, many issues related to the application of ML to real-life problems are yet to be addressed. Here we put forward one limitation which arises from a lack of adaptation of ML models and datasets to specific applications. We formalise a new notion of unfairness as exclusion of opinions. We propose ways to quantify this unfairness, and aid understanding its causes through visualisation. These insights into the functioning of ML-based systems hint at methods to mitigate unfairness.
@article{arxiv.1911.02455,
title = {Unfairness towards subjective opinions in Machine Learning},
author = {Agathe Balayn and Alessandro Bozzon and Zoltan Szlavik},
journal= {arXiv preprint arXiv:1911.02455},
year = {2019}
}
Comments
Human-Centered Machine Learning Perspectives (HCML) workshop at the CHI conference 2019