走向包容性可解释的人工智能:对残疾人工具进行主题分析和范围审查
1Faculty of Business and Information Technology, Ontario Tech University, Oshawa, Ontario, Canada.
Disability and rehabilitation. Assistive technology
|May 27, 2025
概括
可解释的人工智能 (XAI) 工具用于残疾人是有限的,专注于神经疾病. 通过以用户为中心的设计,将XAI扩展到其他障碍,可以改善康复技术和公平护理.
科学领域:
- 医疗保健中的人工智能
- 康复技术 康复技术 康复技术
- 人与计算机的交互
背景情况:
- 可解释的人工智能 (XAI) 提供透明的决策支持,但在残疾人背景下未得到充分利用.
- 目前针对残疾人的XAI应用有限,需要对现有工具和设计原则进行审查.
研究的目的:
- 绘制和评估为残疾人开发的现有XAI工具.
- 在XAI工具设计中识别包容性康复技术的专题模式.
主要方法:
- 在主要数据库 (SCOPUS,ACM,IEEE,ProQuest,Google Scholar) 进行系统的文献搜索 (2018年1月至2024年6月).
- 根据Arksey & O'Malley和PRISMA-ScR指导方针进行最终用户评估的26项同行评审研究的选择.
- 使用Braun&Clarke框架进行主题分析,根据解释方式和设计原则对工具进行分类.
主要成果:
- XAI工具主要关注神经系统疾病 (阿尔茨海默氏症,自闭症,帕金森症),仅限于对骨科,感官或脊柱损伤的关注.
- SHAP,LIME,LRP-B和Grad-CAM是常见的解释模型,可访问性目标包括临床透明度,用户理解和信任.
- 主题分析揭示了建模技术,决策,信任和各种应用环境的维度.
结论:
- 将XAI扩展到代表性不足的残疾人,并纳入多式联运,以用户为中心的解释至关重要.
- 参与式设计,道德监督和标准化评估是提高康复中自主性和个性化的关键.
- 未来的XAI发展应该优先考虑包容性,从而为残疾人提供更有效和公平的护理.
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