透明度(行为)
优势和劣势
人工智能
医疗保健
计算机科学
表(数据库)
人工智能应用
数据科学
知识管理
心理学
政治学
数据挖掘
社会心理学
计算机安全
法学
作者
Carolyn Sun,Shannon L. Harris
标识
DOI:10.1177/14604582241291410
摘要
Objective: Mitigation of racism in artificial intelligence (AI) is needed to improve health outcomes, yet no consensus exists on how this might be achieved. Methods: At an international conference in 2022, experts gathered to discuss strategies for reducing bias in healthcare AI. Results: This paper delineates these strategies along with their corresponding strengths and weaknesses and reviews the existing literature on these strategies. Conclusions: 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.
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