# Fairness at Every Intersection: Uncovering and Mitigating Intersectional Biases in Multimodal Clinical Predictions

arXiv: https://arxiv.org/abs/2412.00606 · HTML: https://mlsanity.com/paper/2412.00606/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 54 references that are in the map.
- First posted on arXiv 30 Nov 2024.

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

- [Publicly Available Clinical BERT Embeddings](https://mlsanity.com/paper/1904.03323/) (2019)
- [Certifying and removing disparate impact](https://mlsanity.com/paper/1412.3756/) (2014)
- [Mitigating Unwanted Biases with Adversarial Learning](https://mlsanity.com/paper/1801.07593/) (2018)
- [Med-BERT: pre-trained contextualized embeddings on large-scale structured electronic health records for disease prediction](https://mlsanity.com/paper/2005.12833/) (2020)
- [Characterizing Intersectional Group Fairness with Worst-Case Comparisons](https://mlsanity.com/paper/2101.01673/) (2021)
- [Multimodal Clinical Benchmark for Emergency Care (MC-BEC): A Comprehensive Benchmark for Evaluating Foundation Models in Emergency Medicine](https://mlsanity.com/paper/2311.04937/) (2023)
- [Anomaly Detection by Robust Statistics](https://mlsanity.com/paper/1707.09752/) (2017)
- [Bayesian Modeling of Intersectional Fairness: The Variance of Bias](https://mlsanity.com/paper/1811.07255/) (2018)

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

- [EQUITRIAGE: A Fairness Audit of Gender Bias in LLM-Based Emergency Department Triage](https://mlsanity.com/paper/2605.03998/) (2026)
- [The Multimodal Paradox: How Added and Missing Modalities Shape Bias and Performance in Multimodal AI](https://mlsanity.com/paper/2505.03020/) (2025)

## Abstract (arXiv)

Biases in automated clinical decision-making using Electronic Healthcare Records (EHR) impose significant disparities in patient care and treatment outcomes. Conventional approaches have primarily focused on bias mitigation strategies stemming from single attributes, overlooking intersectional subgroups -- groups formed across various demographic intersections (such as race, gender, ethnicity, etc.). Rendering single-attribute mitigation strategies to intersectional subgroups becomes statistically irrelevant due to the varying distribution and bias patterns across these subgroups. The multimodal nature of EHR -- data from various sources such as combinations of text, time series, tabular, events, and images -- adds another layer of complexity as the influence on minority groups may fluctuate across modalities. In this paper, we take the initial steps to uncover potential intersectional biases in predictions by sourcing extensive multimodal datasets, MIMIC-Eye1 and MIMIC-IV ED, and propose mitigation at the intersectional subgroup level. We perform and benchmark downstream tasks and bias evaluation on the datasets by learning a unified text representation from multimodal sources, harnessing the enormous capabilities of the pre-trained clinical Language Models (LM), MedBERT, Clinical BERT, and Clinical BioBERT. Our findings indicate that the proposed sub-group-specific bias mitigation is robust across different datasets, subgroups, and embeddings, demonstrating effectiveness in addressing intersectional biases in multimodal settings.