Algorithmic Fairness in Clinical Natural Language Processing: Challenges and Opportunities

Daniel Anadria*, Anastasia Giachanou, Jacqueline Kernahan, Roel Dobbe, Daniel Oberski

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademic

Abstract

The surge in research and development of clinical natural language processing (NLP) has prompted inquiries into the algorithmic fairness of the proposed and deployed technical solutions. In spite of the proliferation of research, limited work has synthesized reflected on the state of algorithmic fairness in clinical NLP. In this short paper, we summarize the findings of our scoping review of literature and present challenges and opportunities in the domain. We identify challenges and opportunities related to studying and measuring protected groups, selecting appropriate methodology, data sharing and privacy, as well as generalizability. The goal of this article is to start a discussion and raise awareness about the gaps encountered within algorithmic fairness in clinical NLP and pave the way for future research.

Original languageEnglish
Title of host publicationProceedings of the 3rd European Workshop on Algorithmic Fairness
PublisherCEUR-WS
Number of pages11
Publication statusPublished - 15 Feb 2025
Event3rd European Workshop on Algorithmic Fairness, EWAF 2024 - Mainz, Germany
Duration: 1 Jul 20243 Jul 2024

Publication series

NameCEUR Workshop Proceedings
Volume3908
ISSN (Print)1613-0073

Conference

Conference3rd European Workshop on Algorithmic Fairness, EWAF 2024
Country/TerritoryGermany
CityMainz
Period1/07/243/07/24

Keywords

  • algorithmic fairness
  • clinical natural language processing
  • NLP in healthcare
  • research gaps

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