人工智能时代的透明度和精度:对可解释性增强的推系统的评估
Jaime Govea1, Rommel Gutierrez1, William Villegas-Ch1
1Escuela de Ingeniería en Ciberseguridad, FICA, Universidad de Las Américas, Quito, Ecuador.
Frontiers in artificial intelligence
|September 20, 2024
概括
将可解释性技术集成到推者系统中可以提高推的准确性和用户的信任. 这种方法提高了透明度,导致更明智的决策和对个性化建议的信心增加.
科学领域:
- 计算机科学 计算机科学
- 人工智能的人工智能
- 人与计算机的交互
背景情况:
- 推系统对于管理信息过载至关重要,但往往缺乏透明度.
- 不透明的算法侵蚀了用户的信任,并可能导致糟糕的决策.
- 可解释性对于用户对个性化系统的信心和知情选择至关重要.
研究的目的:
- 将可解释性技术整合到推者系统中.
- 提高个性化推的准确性和透明度.
- 解决有关用户对复杂推算法的信任问题的问题.
主要方法:
- 使用MovieLens和亚马逊数据集实施和评估推模型.
- 应用可解释性方法,包括LIME (局部可解释的模型不可知解释) 和SHAP (夏普利添加式解释).
- 分析了可解释性对建议准确性和用户理解的影响.
主要成果:
- 在建议准确度方面取得了显著的改进.
- 观察到用户理解和信任系统建议的能力显著增加.
- 通过结合可解释性技术,提升了推准确度3%.
结论:
- 可解释性技术显然可以提高推者系统的性能.
- 整合可解释性提高了用户的信任,并提高了整体用户体验.
- 人工智能系统的透明度是促进用户信心和促进知情决策的关键.
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