# Underestimation Bias and Underfitting in Machine Learning

arXiv: https://arxiv.org/abs/2005.09052 · HTML: https://mlsanity.com/paper/2005.09052/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 May 2020.

## Built on by (most cited first, 2 of 2)

- [Algorithmic Factors Influencing Bias in Machine Learning](https://mlsanity.com/paper/2104.14014/) (2021)
- [Using Pareto Simulated Annealing to Address Algorithmic Bias in Machine Learning](https://mlsanity.com/paper/2105.15064/) (2021)

## Abstract (arXiv)

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.