走向以人为中心的可解释人工智能:对模型解释的用户研究的调查
IEEE transactions on pattern analysis and machine intelligence
|November 13, 2023
概括
可解释的人工智能 (XAI) 用户研究很少,缺乏来自社会科学的见解. 本综述提出了更好的以人为中心的XAI评估的指导方针,强调信任,理解和合作.
科学领域:
- 人与计算机的交互 (HCI)
- 人工智能 (AI) 是一种人工智能.
- 认知科学 认知科学
背景情况:
- 可解释的人工智能 (XAI) 对人工智能研究进步至关重要.
- 了解用户需求和以人为中心的评估是XAI的关键挑战.
- 需要进行系统审查,以评估目前的XAI用户研究实践.
研究的目的:
- 系统地审查和分析XAI应用中的用户研究.
- 根据测量特征 (如信任,理解,可用性和人类-人工智能合作) 来对XAI评估进行分类.
- 提出设计和进行XAI.用户研究的实用指南.
主要方法:
- 在过去五年中,对97篇核心论文进行了系统的文献综述,并对人类进行了基于XAI的评估.
- 基于解释方法和测量用户特征的研究分类.
- 分析最佳实践,常用模型,设计选择和用户研究中的措施.
主要成果:
- XAI的采用在各个领域各不相同,在推系统等领域的传播速度更快.
- 用户在XAI中的评价相对稀少,并未充分利用认知和社会科学的洞察力.
- 在现有用户研究中确定了共同的模型,设计选择和措施.
结论:
- 需要在XAI中进行更强大和跨学科的用户研究.
- 建议提供实用指南,以改善XAI用户研究的设计和执行.
- 未来的研究应该专注于将心理学科学与以人为中心的XAI整合起来.
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