Hunter-Bellevue School of Nursing, Hunter College, New York, NY, USA.
Supply Chain Management and Analytics, School of Business, Virginia Commonwealth University, Richmond, VA, USA.
Health Informatics J. 2024 Oct-Dec;30(4):14604582241291410. doi: 10.1177/14604582241291410.
Mitigation of racism in artificial intelligence (AI) is needed to improve health outcomes, yet no consensus exists on how this might be achieved. At an international conference in 2022, experts gathered to discuss strategies for reducing bias in healthcare AI. This paper delineates these strategies along with their corresponding strengths and weaknesses and reviews the existing literature on these strategies. Five major themes resulted: reducing dataset bias, accurate modeling of existing data, transparency of artificial intelligence, regulation of artificial intelligence and the people who develop it, and bringing stakeholders to the table.
需要减轻人工智能中的种族主义，以改善健康结果，但目前尚不清楚如何实现这一目标。在 2022 年的一次国际会议上，专家们聚集在一起讨论减少医疗保健人工智能偏见的策略。本文阐述了这些策略，以及它们各自的优缺点，并回顾了这些策略的现有文献。结果产生了五个主要主题：减少数据集偏差、准确建模现有数据、人工智能的透明度、人工智能及其开发者的监管以及让利益相关者参与进来。
