临床AI中的偏见和监督:对决策支持工具和公平框架的审查
Farrah Adegunle1, Karanjot Chhatwal2, Sammy Arab2,3
1School of Medical Sciences, University of Manchester, Manchester, UK.
Journal of general internal medicine
|February 2, 2026
概括
人工智能 (AI) 决策支持工具 (DST) 由于偏见而导致医疗保健差异的风险. 在人工智能生命周期中结构性地嵌入公平性对于公平的工具开发和实施至关重要.
科学领域:
- 医疗信息学 医疗信息学
- 医学伦理 医学伦理
- 医疗保健中的人工智能
背景情况:
- 人工智能 (AI) 决策支持工具 (DST) 在临床环境中很普遍.
- 这些工具可能会加剧医疗保健的差距,如果不以公平和文化敏感来设计.
研究的目的:
- 在人工智能驱动的DST中探索种族和种族偏见.
- 评估治理框架,以减轻人工智能医疗保健工具中的偏见.
- 评估当前对AI DSTs的监管指南.
主要方法:
- 审查如何种族和种族偏见表现在AI DSTs.
- 检查偏见算法及其影响.
- 人工智能工具性能差异的案例例子.
- 批判性评估国家和国际监管指南.
主要成果:
- 人工智能数据测试中的偏见源于非代表性的培训数据,缺乏股权审计,以及缺乏强制性的透明度.
- 现有的治理框架往往是非强制性的,缺乏对公平的核心关注.
- 目前英国和美国的监管模式是分散的,不足以检测或防止偏见.
结论:
- 公平必须在整个AI生命周期中结构性地嵌入,以防止有偏见的工具.
- 标准化小组业绩报告和强制性公平性评估是必要的.
- 需要国家和全球治理标准来确保公平的AI工具部署.
相关概念视频
Confirmation Biases
8.2K
The confirmation bias is the tendency to focus on information that confirms our existing beliefs and ignore information that is inconsistent with our expectations. For example, if you think that your professor is not very nice, you notice all of the instances of rude behavior exhibited by the professor while ignoring the countless pleasant interactions he is involved in on a daily basis. Have you ever fallen prey to the confirmation bias, either as the source or target of such bias?
8.2K
Hindsight Biases
4.3K
Hindsight bias leads you to believe that the event you just experienced was predictable, even though it really wasn’t. In other words, you knew all along that things would turn out the way they did. Can you relate this to the phrase "Hindsight is 20/20" now?
4.3K
Equity Theory
301
Equity theory explains how our sense of fairness influences the dynamics of close relationships. Rooted in social psychology, the theory posits that individuals evaluate fairness by comparing the ratio of their contributions to the rewards they receive. Relationship satisfaction is highest when these ratios are perceived as balanced between partners, promoting mutual reciprocity and a sense of justice.Equity vs. Equality in RelationshipsEquity is distinct from equality. Fairness does not...
301
Bias
7.4K
Bias refers to any tendency that prevents a question from being considered unprejudiced. In research, bias occurs when one outcome or answer is selected or encouraged over others in sampling or testing. Bias can occur during any research phase, including study design, data collection, analysis, and publication.
In statistics, a sampling bias is created when a sample is collected from a population, and some members of the population are not as likely to be chosen as others (remember, each member...
In statistics, a sampling bias is created when a sample is collected from a population, and some members of the population are not as likely to be chosen as others (remember, each member...
7.4K
Review and Preview
8.4K
In statistics, several tools are used to interpret the data. Measures of central tendency represent the characteristics of the data, such as mean, median, and mode. Additionally, measures of variance like standard deviation and range are used to find the spread of data from the mean. Relative standing measures the distance between data locations. Commonly used measures of relative standings are percentile, z score, and quartiles.
Percentiles are a type of fractile that partition data into...
Percentiles are a type of fractile that partition data into...
8.4K
Review and Preview
11.3K
Data are individual items of information obtained from a population or sample. Data may be classified as qualitative (categorical), quantitative continuous, or quantitative discrete. Because it is not practical to measure the entire population in a study, researchers use samples to represent the population. A random sample is a representative group from the population chosen by using a method that gives each individual in the population an equal chance of being included in the sample. Random...
11.3K


