论文标题
量化数据和AI模型引起的健康不平等现象
Quantifying Health Inequalities Induced by Data and AI Models
论文作者
论文摘要
人工智能技术正在越来越多地在包括医疗保健在内的关键环境中进行测试和应用。没有有效的方法来检测和减轻AI引起的不平等，AI可能弊大于利，可能导致潜在不平等的扩大。本文提出了一个通用分配终止框架，用于检测和量化AI引起的不平等。具体而言，AI诱导的不平等现象被定量为两个分配降低曲线之间的面积。为了评估框架的性能，对从Hirid产生的十个合成数据集（N> 33,000）进行了实验 - 现实世界中重症监护室（ICU）数据集，显示其准确检测和量化不平等与控制不平等的不平等。进行了广泛的分析以量化嵌入两个现实世界ICU数据集中的健康不平等（a）； （b）由AI模型引起的，该模型训练了两个资源分配方案。结果表明，与男性相比，女性在接受Hirid ICU时的预后标记中降低了33％。与白人患者相比，评估的所有四个AI模型均可诱发非白人的明显不平等（2.45％至43.2％）。这些模型加剧了在8个评估中的3个评估中，嵌入了不平等的数据显着，其中之一> 9倍。 该代码库位于https://github.com/knowlab/daindex-framework。
AI technologies are being increasingly tested and applied in critical environments including healthcare. Without an effective way to detect and mitigate AI induced inequalities, AI might do more harm than good, potentially leading to the widening of underlying inequalities. This paper proposes a generic allocation-deterioration framework for detecting and quantifying AI induced inequality. Specifically, AI induced inequalities are quantified as the area between two allocation-deterioration curves. To assess the framework's performance, experiments were conducted on ten synthetic datasets (N>33,000) generated from HiRID - a real-world Intensive Care Unit (ICU) dataset, showing its ability to accurately detect and quantify inequality proportionally to controlled inequalities. Extensive analyses were carried out to quantify health inequalities (a) embedded in two real-world ICU datasets; (b) induced by AI models trained for two resource allocation scenarios. Results showed that compared to men, women had up to 33% poorer deterioration in markers of prognosis when admitted to HiRID ICUs. All four AI models assessed were shown to induce significant inequalities (2.45% to 43.2%) for non-White compared to White patients. The models exacerbated data embedded inequalities significantly in 3 out of 8 assessments, one of which was >9 times worse. The codebase is at https://github.com/knowlab/DAindex-Framework.
