# Algorithmic Factors Influencing Bias in Machine Learning

arXiv: https://arxiv.org/abs/2104.14014 · HTML: https://mlsanity.com/paper/2104.14014/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 25 references that are in the map.
- First posted on arXiv 28 Apr 2021.

## Builds on (most cited first, 8 of 8)

- [Equality of Opportunity in Supervised Learning](https://mlsanity.com/paper/1610.02413/) (2016)
- [Counterfactual Fairness](https://mlsanity.com/paper/1703.06856/) (2017)
- [Certifying and removing disparate impact](https://mlsanity.com/paper/1412.3756/) (2014)
- [What Do Compressed Deep Neural Networks Forget?](https://mlsanity.com/paper/1911.05248/) (2019)
- [Characterising Bias in Compressed Models](https://mlsanity.com/paper/2010.03058/) (2020)
- [Good Counterfactuals and Where to Find Them: A Case-Based Technique for Generating Counterfactuals for Explainable AI (XAI)](https://mlsanity.com/paper/2005.13997/) (2020)
- [Auditing Black-box Models for Indirect Influence](https://mlsanity.com/paper/1602.07043/) (2016)
- [Underestimation Bias and Underfitting in Machine Learning](https://mlsanity.com/paper/2005.09052/) (2020)

## Built on by (most cited first, 9 of 9)

- [Understanding Unfairness in Fraud Detection through Model and Data Bias Interactions](https://mlsanity.com/paper/2207.06273/) (2022)
- [Prisoners of Their Own Devices: How Models Induce Data Bias in Performative Prediction](https://mlsanity.com/paper/2206.13183/) (2022)
- [Stock Embeddings: Learning Distributed Representations for Financial Assets](https://mlsanity.com/paper/2202.08968/) (2022)
- [Using Pareto Simulated Annealing to Address Algorithmic Bias in Machine Learning](https://mlsanity.com/paper/2105.15064/) (2021)
- [Trustworthy and Responsible AI for Human-Centric Autonomous Decision-Making Systems](https://mlsanity.com/paper/2408.15550/) (2024)
- [Enhancing Fairness and Performance in Machine Learning Models: A Multi-Task Learning Approach with Monte-Carlo Dropout and Pareto Optimality](https://mlsanity.com/paper/2404.08230/) (2024)
- [Outlier Detection Bias Busted: Understanding Sources of Algorithmic Bias through Data-centric Factors](https://mlsanity.com/paper/2408.13667/) (2024)
- [Introducing a Family of Synthetic Datasets for Research on Bias in Machine Learning](https://mlsanity.com/paper/2107.08928/) (2021)
- [A Comparative Analysis of Bias Amplification in Graph Neural Network Approaches for Recommender Systems](https://mlsanity.com/paper/2301.07639/) (2023)

## Abstract (arXiv)

It is fair to say that many of the prominent examples of bias in Machine Learning (ML) arise from bias that is there in the training data. In fact, some would argue that supervised ML algorithms cannot be biased, they reflect the data on which they are trained. In this paper we demonstrate how ML algorithms can misrepresent the training data through underestimation. We show how irreducible error, regularization and feature and class imbalance can contribute to this underestimation. The paper concludes with a demonstration of how the careful management of synthetic counterfactuals can ameliorate the impact of this underestimation bias.