# Bias in Machine Learning Models Can Be Significantly Mitigated by Careful Training: Evidence from Neuroimaging Studies

arXiv: https://arxiv.org/abs/2205.13421 · HTML: https://mlsanity.com/paper/2205.13421/

## Key facts

- Cited by 14 papers in the map (data through 13 Oct 2026).
- Builds on 10 of its 54 references that are in the map.
- First posted on arXiv 26 May 2022.

## Builds on (most cited first, 10 of 10)

- [Equality of Opportunity in Supervised Learning](https://mlsanity.com/paper/1610.02413/) (2016)
- [A Reductions Approach to Fair Classification](https://mlsanity.com/paper/1803.02453/) (2018)
- [AutoGluon-Tabular: Robust and Accurate AutoML for Structured Data](https://mlsanity.com/paper/2003.06505/) (2020)
- [Convolutional Neural Networks for Classification of Alzheimer's Disease: Overview and Reproducible Evaluation](https://mlsanity.com/paper/1904.07773/) (2019)
- [A Unified Approach to Quantifying Algorithmic Unfairness: Measuring Individual & Group Unfairness via Inequality Indices](https://mlsanity.com/paper/1807.00787/) (2018)
- [Fairness Metrics: A Comparative Analysis](https://mlsanity.com/paper/2001.07864/) (2020)
- [Embracing the Disharmony in Medical Imaging: A Simple and Effective Framework for Domain Adaptation](https://mlsanity.com/paper/2103.12857/) (2021)
- [Feature robustness and sex differences in medical imaging: a case study in MRI-based Alzheimer's disease detection](https://mlsanity.com/paper/2204.01737/) (2022)
- [Harmonization with Flow-based Causal Inference](https://mlsanity.com/paper/2106.06845/) (2021)
- [DeepMRSeg: A convolutional deep neural network for anatomy and abnormality segmentation on MR images](https://mlsanity.com/paper/1907.02110/) (2019)

## Built on by (most cited first, 14 of 14)

- [Addressing Fairness Issues in Deep Learning-Based Medical Image Analysis: A Systematic Review](https://mlsanity.com/paper/2209.13177/) (2022)
- [Bias in Unsupervised Anomaly Detection in Brain MRI](https://mlsanity.com/paper/2308.13861/) (2023)
- [A Review of Fairness and A Practical Guide to Selecting Context-Appropriate Fairness Metrics in Machine Learning](https://mlsanity.com/paper/2411.06624/) (2024)
- [Gender Fairness of Machine Learning Algorithms for Pain Detection](https://mlsanity.com/paper/2506.11132/) (2025)
- [Adapting Machine Learning Diagnostic Models to New Populations Using a Small Amount of Data: Results from Clinical Neuroscience](https://mlsanity.com/paper/2308.03175/) (2023)
- [Quantifying the Impact of Population Shift Across Age and Sex for Abdominal Organ Segmentation](https://mlsanity.com/paper/2408.04610/) (2024)
- [Invisible Attributes, Visible Biases: Exploring Demographic Shortcuts in MRI-based Alzheimer's Disease Classification](https://mlsanity.com/paper/2509.09558/) (2025)
- [The Voice of Equity: A Systematic Evaluation of Bias Mitigation Techniques for Speech-Based Cognitive Impairment Detection Across Architectures and Demographics](https://mlsanity.com/paper/2601.16989/) (2026)
- [Generative models of MRI-derived neuroimaging features and associated dataset of 18,000 samples](https://mlsanity.com/paper/2407.12897/) (2024)
- [More is Less? A Simulation-Based Approach to Dynamic Interactions between Biases in Multimodal Models](https://mlsanity.com/paper/2412.17505/) (2024)
- [Understanding-informed Bias Mitigation for Fair CMR Segmentation](https://mlsanity.com/paper/2503.17089/) (2025)
- [An Investigation Into Race Bias in Random Forest Models Based on Breast DCE-MRI Derived Radiomics Features](https://mlsanity.com/paper/2309.17197/) (2023)
- [Equity in Healthcare: Analyzing Disparities in Machine Learning Predictions of Diabetic Patient Readmissions](https://mlsanity.com/paper/2403.19057/) (2024)
- [Structural Alignment for Reliable Industrial AI: Bridging Physical Reality, Data, Models, and Human Intent](https://mlsanity.com/paper/2609.35400/) (2026)

## Abstract (arXiv)

Despite the great promise that machine learning has offered in many fields of medicine, it has also raised concerns about potential biases and poor generalization across genders, age distributions, races and ethnicities, hospitals, and data acquisition equipment and protocols. In the current study, and in the context of three brain diseases, we provide evidence which suggests that when properly trained, machine learning models can generalize well across diverse conditions and do not necessarily suffer from bias. Specifically, by using multi-study magnetic resonance imaging consortia for diagnosing Alzheimer's disease, schizophrenia, and autism spectrum disorder, we find that well-trained models have a high area-under-the-curve (AUC) on subjects across different subgroups pertaining to attributes such as gender, age, racial groups, and different clinical studies and are unbiased under multiple fairness metrics such as demographic parity difference, equalized odds difference, equal opportunity difference etc. We find that models that incorporate multi-source data from demographic, clinical, genetic factors and cognitive scores are also unbiased. These models have better predictive AUC across subgroups than those trained only with imaging features but there are also situations when these additional features do not help.