人类中的解释策略与当前可解释的人工智能:从图像分类的洞察力
Ruoxi Qi1, Yueyuan Zheng1,2, Yi Yang2
1Department of Psychology, University of Hong Kong, Hong Kong SAR, China.
British journal of psychology (London, England : 1953)
|June 11, 2024
概括
可解释的人工智能 (XAI) 方法在突出可观察的因果关系时,与人类解释策略的相似性更高. 这表明,通过更好地与人类解释他们的推理方式相协调,XAI可以更容易获得.
科学领域:
- 认知科学 认知科学
- 人工智能的人工智能
- 人与计算机的交互
背景情况:
- 可解释的人工智能 (XAI) 方法为人工智能决策提供了洞察力,但它们与人类解释过程的协调程度尚不清楚.
- 将XAI输出与人类认知策略进行比较对于开发更直观,更有效的AI系统至关重要.
- 当前的XAI技术往往缺乏与人类自然解释他们的推理方式的明确联系.
研究的目的:
- 在使用眼球追踪的图像分类和解释任务中调查人类注意力策略.
- 将人类注意力模式与当前XAI方法产生的基于突出性的解释进行比较.
- 为了确定哪些XAI解释类型最像人类解释策略.
主要方法:
- 人类参与者分类图像并解释他们的分类.
- 眼睛追踪技术在两个任务期间监控了参与者的视觉注意力.
- 来自XAI方法的度图与人类注意力数据进行了比较.
主要成果:
- 与分类相比,人类在解释时使用了更多的探索性注意力策略.
- 两种不同的人类解释策略出现了:以概念解释为中心的扫描和以视觉解释为探索性扫描.
- XAI显著性地图最接近人类探索性注意力策略.
- 通过扰动强调可观测的因果关系的XAI解释与关注内部AI特征的人类策略相比,更符合人类策略.
结论:
- 人类解释整合了视觉和概念信息,服务于不同的功能.
- 突出可观察的因果关系的XAI方法与人类解释策略的相似性更大.
- 将XAI与人类认知过程结合起来,可以提高用户对AI的可访问性和理解.
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