@inproceedings{a77d0fb2990047d3a475b4f8f0d55d70,
title = "Underestimation Bias and Underfitting in Machine Learning",
abstract = "Often, what is termed algorithmic bias in machine learning will be due to historic bias in the training data. But sometimes the bias may be introduced (or at least exacerbated) by the algorithm itself. The ways in which algorithms can actually accentuate bias has not received a lot of attention with researchers focusing directly on methods to eliminate bias - no matter the source. In this paper we report on initial research to understand the factors that contribute to bias in classification algorithms. We believe this is important because underestimation bias is inextricably tied to regularization, i.e. measures to address overfitting can accentuate bias.",
keywords = "Algorithmic bias, Machine Learning",
author = "P{\'a}draig Cunningham and Delany, {Sarah Jane}",
note = "Publisher Copyright: {\textcopyright} 2021, Springer Nature Switzerland AG.; 1st International Workshop on Trustworthy AI – Integrating Learning, Optimization and Reasoning, TAILOR 2020 held as a part of European Conference on Artificial Intelligence, ECAI 2020 ; Conference date: 04-09-2020 Through 05-09-2020",
year = "2021",
doi = "10.1007/978-3-030-73959-1_2",
language = "English",
isbn = "9783030739584",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Science and Business Media Deutschland GmbH",
pages = "20--31",
editor = "Fredrik Heintz and Michela Milano and Barry O{\textquoteright}Sullivan",
booktitle = "Trustworthy AI – Integrating Learning, Optimization and Reasoning - First International Workshop, TAILOR 2020, Revised Selected Papers",
address = "Germany",
}