# Addressing Fairness, Bias and Class Imbalance in Machine Learning: the FBI-loss

arXiv: https://arxiv.org/abs/2105.06345 · HTML: https://mlsanity.com/paper/2105.06345/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 20 of its 78 references that are in the map.
- First posted on arXiv 13 May 2021.

## Builds on (most cited first, 20 of 20)

- ["Why Should I Trust You?": Explaining the Predictions of Any Classifier](https://mlsanity.com/paper/1602.04938/) (2016)
- [Domain-Adversarial Training of Neural Networks](https://mlsanity.com/paper/1505.07818/) (2015)
- [Rich feature hierarchies for accurate object detection and semantic segmentation](https://mlsanity.com/paper/1311.2524/) (2013)
- [Equality of Opportunity in Supervised Learning](https://mlsanity.com/paper/1610.02413/) (2016)
- [A Survey on Bias and Fairness in Machine Learning](https://mlsanity.com/paper/1908.09635/) (2019)
- [Men Also Like Shopping: Reducing Gender Bias Amplification using Corpus-level Constraints](https://mlsanity.com/paper/1707.09457/) (2017)
- [Right for the Right Reasons: Training Differentiable Models by Constraining their Explanations](https://mlsanity.com/paper/1703.03717/) (2017)
- [Learning Not to Learn: Training Deep Neural Networks with Biased Data](https://mlsanity.com/paper/1812.10352/) (2018)
- [Causality matters in medical imaging](https://mlsanity.com/paper/1912.08142/) (2019)
- [Controllable Invariance through Adversarial Feature Learning](https://mlsanity.com/paper/1705.11122/) (2017)
- [Invariant Representations without Adversarial Training](https://mlsanity.com/paper/1805.09458/) (2018)
- [Deep Over-sampling Framework for Classifying Imbalanced Data](https://mlsanity.com/paper/1704.07515/) (2017)
- [Two-Player Games for Efficient Non-Convex Constrained Optimization](https://mlsanity.com/paper/1804.06500/) (2018)
- [Scalable Object Detection using Deep Neural Networks](https://mlsanity.com/paper/1312.2249/) (2013)
- [Machine learning in policy evaluation: new tools for causal inference](https://mlsanity.com/paper/1903.00402/) (2019)
- [Representation Learning with Statistical Independence to Mitigate Bias](https://mlsanity.com/paper/1910.03676/) (2019)
- [Convolutional Neural Networks based Focal Loss for Class Imbalance Problem: A Case Study of Canine Red Blood Cells Morphology Classification](https://mlsanity.com/paper/2001.03329/) (2020)
- [Transfer learning with class-weighted and focal loss function for automatic skin cancer classification](https://mlsanity.com/paper/2009.05977/) (2020)
- [Measuring the effects of confounders in medical supervised classification problems: the Confounding Index (CI)](https://mlsanity.com/paper/1905.08871/) (2019)
- [Focal Loss Dense Detector for Vehicle Surveillance](https://mlsanity.com/paper/1803.01114/) (2018)

## Built on by (most cited first, 6 of 6)

- [Towards A Holistic View of Bias in Machine Learning: Bridging Algorithmic Fairness and Imbalanced Learning](https://mlsanity.com/paper/2207.06084/) (2022)
- [Improved Input Reprogramming for GAN Conditioning](https://mlsanity.com/paper/2201.02692/) (2022)
- [Properties of fairness measures in the context of varying class imbalance and protected group ratios](https://mlsanity.com/paper/2411.08425/) (2024)
- [Fair Oversampling Technique using Heterogeneous Clusters](https://mlsanity.com/paper/2305.13875/) (2023)
- [Validity of Feature Importance in Low-Performing Machine Learning for Tabular Biomedical Data](https://mlsanity.com/paper/2409.13342/) (2024)
- [Multi-Level Analyzation of Imbalance to Resolve Non-IID-Ness in Federated Learning](https://mlsanity.com/paper/2606.10250/) (2026)

## Abstract (arXiv)

Resilience to class imbalance and confounding biases, together with the assurance of fairness guarantees are highly desirable properties of autonomous decision-making systems with real-life impact. Many different targeted solutions have been proposed to address separately these three problems, however a unifying perspective seems to be missing. With this work, we provide a general formalization, showing that they are different expressions of unbalance. Following this intuition, we formulate a unified loss correction to address issues related to Fairness, Biases and Imbalances (FBI-loss). The correction capabilities of the proposed approach are assessed on three real-world benchmarks, each associated to one of the issues under consideration, and on a family of synthetic data in order to better investigate the effectiveness of our loss on tasks with different complexities. The empirical results highlight that the flexible formulation of the FBI-loss leads also to competitive performances with respect to literature solutions specialised for the single problems.