Exploring Text Specific and Blackbox Fairness Algorithms in Multimodal Clinical NLP

John Chen, Ian Berlot-Attwell, Xindi Wang, Safwan Hossain, Frank Rudzicz


Abstract
Clinical machine learning is increasingly multimodal, collected in both structured tabular formats and unstructured forms such as free text. We propose a novel task of exploring fairness on a multimodal clinical dataset, adopting equalized odds for the downstream medical prediction tasks. To this end, we investigate a modality-agnostic fairness algorithm - equalized odds post processing - and compare it to a text-specific fairness algorithm: debiased clinical word embeddings. Despite the fact that debiased word embeddings do not explicitly address equalized odds of protected groups, we show that a text-specific approach to fairness may simultaneously achieve a good balance of performance classical notions of fairness. Our work opens the door for future work at the critical intersection of clinical NLP and fairness.
Anthology ID:
2020.clinicalnlp-1.33
Volume:
Proceedings of the 3rd Clinical Natural Language Processing Workshop
Month:
November
Year:
2020
Address:
Online
Venues:
ClinicalNLP | EMNLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
301–312
Language:
URL:
https://www.aclweb.org/anthology/2020.clinicalnlp-1.33
DOI:
10.18653/v1/2020.clinicalnlp-1.33
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PDF:
http://aclanthology.lst.uni-saarland.de/2020.clinicalnlp-1.33.pdf