Document Type
Conference Paper
Rights
Available under a Creative Commons Attribution Non-Commercial Share Alike 4.0 International Licence
Disciplines
1.2 COMPUTER AND INFORMATION SCIENCE
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.
DOI
https://doi.org/10.1007/978-3-030-73959-1_2
Recommended Citation
Cunningham, P. & Delaney. S.J. (2021). Underestimation Bias and Underfitting in Machine Learning. International Workshop on the Foundations of Trustworthy AI Integrating Learning, Optimization and Reasoning, 4-5 September, Santiago de Compestela, Spain. doi:10.1007/978-3-030-73959-1_2
Funder
This work was funded by Science Foundation Ireland through the SFI Centre for Research Training in Machine Learning (Grant No. 18/CRT/6183).
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-Share Alike 4.0 International License.