人工智能在医疗保健中的偏见:基于网络的调查
Carina Nina Vorisek1, Caroline Stellmach1, Paula Josephine Mayer1
1Core Facility Digital Medicine and Interoperability, Berlin Institute of Health at Charité - Universitätsmedizin Berlin, Berlin, Germany.
Journal of medical Internet research
|June 22, 2023
概括
医疗保健领域的人工智能开发人员认为人工智能偏见中等公平,性别少数群体认为他们的人工智能开发不公平. 需要进一步研究少数群体对人工智能的看法和偏见预防.
科学领域:
- 医疗人工智能 医疗人工智能
- 医疗保健技术 技术 医疗保健 技术
- 人工智能伦理学
背景情况:
- 越来越多的人工智能 (AI) 投资用于诊断和治疗等医疗应用.
- 之前的研究涉及人工智能透明度和偏见减少,但开发人员的看法仍未得到充分研究.
- 对人工智能开发人员的知识和对医疗保健偏见的观点的有限理解.
研究的目的:
- 调查医疗保健人工智能专家关于他们对人工智能算法的偏见的看法.
- 调查开发人员对偏见预防措施的认识和使用情况.
- 根据人口统计和工作环境来评估观念的变化.
主要方法:
- 在德语和英语中进行了多达41个问题的基于网络的调查.
- 分析包括人口统计数据,技术专长,以及对公平和偏见的看法.
- 分析了那些有医疗AI经验和完整问卷的参与者数据.
主要成果:
- 151名人工智能专家完成了调查,67%是男性,平均年龄为30岁.
- 31%的人认为他们的人工智能项目是公平的,34%的人认为是中等公平的,12%的人认为是不公平的.
- 确定了主要的偏见贡献者:缺乏公平的数据 (68%),指导方针不足 (49%) 和知识差距 (45%).
结论:
- 开发人员对人工智能偏见的看法是适度公平的,但性别少数群体报告的公平性评级较低.
- 需要更多地关注少数民族和女性在人工智能开发中的观点.
- 加强知识和指导方针,以防止医疗保健中的偏见AI至关重要.
关键词:
在这里,我们可以看到AIAIAI.公平的数据是公平的.年龄的年龄年龄的年龄.申请申请表 申请表 申请表人工智能的人工智能是人工智能.偏见 偏见 偏见 偏见 偏见临床临床临床临床临床临床临床深度学习是一种深度学习.发展发展发展发展发展.诊断 诊断 诊断 诊断 诊断 诊断数字健康数字健康疾病 疾病 疾病 疾病性别 性别 性别 性别 性别医疗保健 医疗保健 医疗保健机器学习是机器学习.在线在线在线 在线在线在线在线预防 预防 预防调查调查调查调查调查调查调查调查治疗治疗治疗治疗治疗治疗更多相关视频
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