通过三种透明度解释，发现中等透明度最利于用户信任与交互提升。
Understanding the Effect of Algorithm Transparency of Model Explanations in Text-to-SQL Semantic Parsing
- 设计低、中、高三种模型决策透明度解释方式。
- 中等透明组用户表现持续提升且信任波动最小。
- 适合希望提升人机协作效率的AI系统设计者。
解释AI决策对建立用户信任至关重要。本文研究文本转SQL语义解析任务中模型解释的算法透明度影响。设计三种不同透明度的解释（低、中、高），考察其对用户体验的影响。基于约100名参与者的研究表明：（1）低透明度导致用户依赖度降低，高透明度则提高依赖；中等透明度能实现平衡。（2）仅中等透明度组在交互中表现持续提升。（3）该组用户在研究前后信任变化最小，稳定性最佳。
原文摘要 · Abstract (English)
Explaining the decisions of AI has become vital for fostering appropriate user trust in these systems. This paper investigates explanations for a structured prediction task called ``text-to-SQL Semantic Parsing'', which translates a natural language question into a structured query language (SQL) program. In this task setting, we designed three levels of model explanation, each exposing a different amount of the model's decision-making details (called ``algorithm transparency''), and investigated how different model explanations could potentially yield different impacts on the user experience. Our study with $\sim$100 participants shows that (1) the low-/high-transparency explanations often lead to less/more user reliance on the model decisions, whereas the medium-transparency explanations strike a good balance. We also show that (2) only the medium-transparency participant group was able to engage further in the interaction and exhibit increasing performance over time, and that (3) they showed the least changes in trust before and after the study.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。