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

arXiv:2412.00606 · cs.AI · Submitted 2024-11-30 · Read on arXiv

cs.AI

Submitted: 2024-11-30

Updated: 2026-09-18

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

• Embeddings — 16× in this paper · explained in 2 episodes
• Downstream Tasks — 7× in this paper · explained in 2 episodes
• Intersectionality — 7× in this paper · explained in 2 episodes
• Benchmark — 6× in this paper · explained in 2 episodes
• Multimodal Learning — 5× in this paper · explained in 2 episodes
• F1 Score — 3× in this paper · explained in 3 episodes
• Language Models — 3× in this paper
• Multimodal Dataset — 3× in this paper · explained in 2 episodes
• Representation Learning — 3× in this paper · explained in 2 episodes
• Accuracy — 2× in this paper · explained in 1 episode
• Benchmarking — 2× in this paper · explained in 3 episodes
• Demographic Parity — 2× in this paper
• Electronic Healthcare Records — 2× in this paper
• Reject Option Classification — 2× in this paper
• Subgroup-specific Discrimination Aware Ensembling — 2× in this paper
• Adaptability — 1× in this paper · explained in 1 episode
• Adversarial Debiasing — 1× in this paper · explained in 1 episode
• Ensemble Approach — 1× in this paper · explained in 3 episodes
• Foundation Model — 1× in this paper · explained in 3 episodes
• Foundation Models — 1× in this paper · explained in 7 episodes
• Robustness — 1× in this paper · explained in 5 episodes
• Vector Representations — 1× in this paper · explained in 2 episodes

Sources

• CLIP the Bias: How Useful is Balancing Data in Multimodal Learning?
• Publicly Available Clinical BERT Embeddings
• Emergency Department Decision Support using Clinical Pseudo-notes

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