Summary of Fairness at Every Intersection: Uncovering and Mitigating Intersectional Biases in Multimodal Clinical Predictions, by Resmi Ramachandranpillai et al.
Fairness at Every Intersection: Uncovering and Mitigating Intersectional Biases in Multimodal Clinical Predictions
by Resmi Ramachandranpillai, Kishore Sampath, Ayaazuddin Mohammad, Malihe Alikhani
First submitted to arxiv on: 30 Nov 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper addresses the issue of biases in automated clinical decision-making using Electronic Healthcare Records (EHR). Conventional approaches have focused on mitigating biases stemming from single attributes, neglecting intersectional subgroups. The paper proposes a novel approach to mitigate biases at the intersectional subgroup level by learning a unified text representation from multimodal sources and harnessing pre-trained clinical Language Models (LM) such as MedBERT, Clinical BERT, and Clinical BioBERT. The authors benchmark downstream tasks and bias evaluation on extensive multimodal datasets MIMIC-Eye1 and MIMIC-IV ED. They find that the proposed method is robust across different datasets, subgroups, and embeddings, effectively addressing intersectional biases in multimodal settings. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps make medical decisions better by fixing problems with how computers use health records. Right now, these systems can be unfair to certain groups of people because they’re based on single characteristics like race or gender. The authors created a new way to look at this problem and tested it using lots of different types of data from hospitals. They used special computer programs to learn about patterns in the data and found that their method works well for all kinds of patients. |
Keywords
» Artificial intelligence » Bert » Stemming
