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

• URL: http://arxiv.org/abs/2412.00606v1
• Date: Sat, 30 Nov 2024 22:53:11 GMT
• Title: Fairness at Every Intersection: Uncovering and Mitigating Intersectional Biases in Multimodal Clinical Predictions
• Abstract summary: We take the initial steps to uncover potential intersectional biases in predictions by sourcing extensive multimodal datasets.<n>We perform and benchmark downstream tasks and bias evaluation on the datasets by learning a unified text representation from multimodal sources.<n>Our findings indicate that the proposed sub-group-specific bias mitigation is robust across different datasets.
• Score: 17.578593616991927
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

Related papers

• FairSelect: A Systematic Evaluation of Multi-Level and Intersectional Algorithmic Fairness [0.5295211483828045]
  Algorithmic fairness methods are increasingly used to identify and mitigate bias in machine learning models.<n>This work presents FairSelect, a toolkit for evaluating fairness mitigation strategies applied individually and in combination.
  arXiv  Detail & Related papers  (2026-07-09T21:27:29Z)
• MESD: Detecting and Mitigating Procedural Bias in Intersectional Groups [0.0]
  This research addresses an important gap by examining explainability with respect to fairness across multiple protected categories.<n> Experimental results across multiple datasets show that UEF effectively balances objectives.
  arXiv  Detail & Related papers  (2026-03-13T15:42:31Z)
• Group Contrastive Learning for Weakly Paired Multimodal Data [34.76498775412033]
  GROOVE is a semi-supervised multi-modal representation learning approach for high-content perturbation data.<n>GroupCLIP is a novel group-level contrastive loss that bridges the gap between CLIP for paired cross-modal data and SupCon for uni-modal supervised contrastive learning.
  arXiv  Detail & Related papers  (2026-02-03T21:11:06Z)
• Robust Multimodal Representation Learning in Healthcare [12.190907451083765]
  Real-world medical datasets commonly contain systematic biases from multiple sources.<n>We propose a Dual-Stream Feature Decorrelation Framework that identifies and handles the biases.<n>Our method employs a causal-biased decorrelation framework with dual-stream neural networks to disentangle causal features from spurious correlations.
  arXiv  Detail & Related papers  (2026-01-29T16:27:54Z)
• Equitable Electronic Health Record Prediction with FAME: Fairness-Aware Multimodal Embedding [4.383326688441244]
  We introduce FAME (Fairness-Aware Multimodal Embeddings), a framework that explicitly weights each modality according to its fairness contribution.<n>We leverage the Error Distribution Disparity Index (EDDI) to measure fairness across subgroups.<n>We demonstrate FAME's effectiveness in terms of performance and fairness compared with other baselines.
  arXiv  Detail & Related papers  (2025-06-16T05:23:42Z)
• Unsupervised Search for Ethnic Minorities' Medical Segmentation Training Set [5.880582406602758]
  This article investigates the critical issue of dataset bias in medical imaging, with a particular emphasis on racial disparities. Our analysis reveals that medical segmentation datasets are significantly biased, primarily influenced by the demographic composition of their collection sites. We propose a novel training set search strategy aimed at reducing these biases by focusing on underrepresented racial groups.
  arXiv  Detail & Related papers  (2025-01-05T05:04:47Z)
• CEB: Compositional Evaluation Benchmark for Fairness in Large Language Models [58.57987316300529]
  Large Language Models (LLMs) are increasingly deployed to handle various natural language processing (NLP) tasks. To evaluate the biases exhibited by LLMs, researchers have recently proposed a variety of datasets. We propose CEB, a Compositional Evaluation Benchmark that covers different types of bias across different social groups and tasks.
  arXiv  Detail & Related papers  (2024-07-02T16:31:37Z)
• Cross-Care: Assessing the Healthcare Implications of Pre-training Data on Language Model Bias [3.455189439319919]
  We introduce Cross-Care, the first benchmark framework dedicated to assessing biases and real world knowledge in large language models (LLMs) We evaluate how demographic biases embedded in pre-training corpora like $ThePile$ influence the outputs of LLMs. Our results highlight substantial misalignment between LLM representation of disease prevalence and real disease prevalence rates across demographic subgroups.
  arXiv  Detail & Related papers  (2024-05-09T02:33:14Z)
• A structured regression approach for evaluating model performance across intersectional subgroups [53.91682617836498]
  Disaggregated evaluation is a central task in AI fairness assessment, where the goal is to measure an AI system's performance across different subgroups. We introduce a structured regression approach to disaggregated evaluation that we demonstrate can yield reliable system performance estimates even for very small subgroups.
  arXiv  Detail & Related papers  (2024-01-26T14:21:45Z)
• Tackling Diverse Minorities in Imbalanced Classification [80.78227787608714]
  Imbalanced datasets are commonly observed in various real-world applications, presenting significant challenges in training classifiers. We propose generating synthetic samples iteratively by mixing data samples from both minority and majority classes. We demonstrate the effectiveness of our proposed framework through extensive experiments conducted on seven publicly available benchmark datasets.
  arXiv  Detail & Related papers  (2023-08-28T18:48:34Z)
• The Role of Subgroup Separability in Group-Fair Medical Image Classification [18.29079361470428]
  We find a relationship between subgroup separability, subgroup disparities, and performance degradation when models are trained on data with systematic bias such as underdiagnosis. Our findings shed new light on the question of how models become biased, providing important insights for the development of fair medical imaging AI.
  arXiv  Detail & Related papers  (2023-07-06T06:06:47Z)
• Ambiguous Medical Image Segmentation using Diffusion Models [60.378180265885945]
  We introduce a single diffusion model-based approach that produces multiple plausible outputs by learning a distribution over group insights. Our proposed model generates a distribution of segmentation masks by leveraging the inherent sampling process of diffusion. Comprehensive results show that our proposed approach outperforms existing state-of-the-art ambiguous segmentation networks.
  arXiv  Detail & Related papers  (2023-04-10T17:58:22Z)
• Cohort Bias Adaptation in Aggregated Datasets for Lesion Segmentation [0.8466401378239363]
  We propose a generalized affine conditioning framework to learn and account for cohort biases across multi-source datasets. We show that our cohort bias adaptation method improves performance of the network on pooled datasets.
  arXiv  Detail & Related papers  (2021-08-02T08:32:57Z)
• MultiFair: Multi-Group Fairness in Machine Learning [52.24956510371455]
  We study multi-group fairness in machine learning (MultiFair) We propose a generic end-to-end algorithmic framework to solve it. Our proposed framework is generalizable to many different settings.
  arXiv  Detail & Related papers  (2021-05-24T02:30:22Z)
• Adversarial Sample Enhanced Domain Adaptation: A Case Study on Predictive Modeling with Electronic Health Records [57.75125067744978]
  We propose a data augmentation method to facilitate domain adaptation. adversarially generated samples are used during domain adaptation. Results confirm the effectiveness of our method and the generality on different tasks.
  arXiv  Detail & Related papers  (2021-01-13T03:20:20Z)
• Self-Supervised Multimodal Domino: in Search of Biomarkers for Alzheimer's Disease [19.86082635340699]
  We propose a taxonomy of all reasonable ways to organize self-supervised representation-learning algorithms. We first evaluate models on toy multimodal MNIST datasets and then apply them to a multimodal neuroimaging dataset with Alzheimer's disease patients. Results show that the proposed approach outperforms previous self-supervised encoder-decoder methods.
  arXiv  Detail & Related papers  (2020-12-25T20:28:13Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.