将可解释的人工智能方法应用于自动创造力评估模型的应用
Anastasia S Panfilova1, Ekaterina A Valueva1,2, Ivan Y Ilyin3
1Laboratory of Psychology and Psychophisiology of Creativity, Institute of Psychology of the Russian Academy of Science, Moscow, Russia.
Frontiers in artificial intelligence
|October 16, 2024
概括
人工智能模型现在可以使用城市测试来评估绘图中的创造力. 可解释的人工智能证实这些模型与专家评估一致,增强了自动化创造力评估.
科学领域:
- 心理学 心理学 心理学
- 计算机科学 计算机科学
- 人工智能的人工智能
背景情况:
- 评估创造力对于理解人类认知至关重要.
- 城市测试提供了一种标准化的方法,通过绘图来评估创造力.
- 创意的自动评估可以提供客观和可扩展的见解.
研究的目的:
- 用城市测试图纸来比较人工智能 (AI) 模型来确定创造力水平.
- 应用可解释的AI (XAI) 方法来识别影响AI预测的关键绘图特征.
- 将人工智能驱动的创造力评估与专家评估进行验证.
主要方法:
- 使用了1,823个城市测试图纸的数据集.
- 微调预训练的人工智能模型 (MobileNet,ResNet18,AlexNet,DenseNet,ResNext,EfficientNet,ViT) 用于得分预测.
- 使用梯度加权类激活映射 (Grad-CAM) 来可视化模型关注区域.
主要成果:
- 移动网络实现了最高的预测准确率76%.
- XAI分析显示,模型识别的特征与专家城市测试标准相关.
- 对错误预测的分析有助于改进对AI依赖特定绘图特征的理解.
结论:
- 神经网络方法可以通过城市测试自动化创造力水平诊断.
- XAI证实,确定的人工智能激活区域符合专家城市测试评估规则.
- 这种方法为客观的创造力评估提供了一个有前途的工具.
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