通过个性化调整可解释模型，让不同用户获得最适合自己的解释方式。
Navigating the Rashomon Effect: How Personalization Can Help Adjust Interpretable Machine Learning Models to Individual Users
- 用上下文相关强化学习动态优化可解释模型配置
- 108名用户实验显示个性化带来差异化的模型设置
- 保持高可解释性，适合关注模型透明度的使用者
Rashomon效应指出，机器学习中多个模型可能具备相似预测性能却以不同方式解释数据关系，这一现象在广义加性模型（GAMs）等内在可解释模型中同样存在。本文提出一种基于上下文带轮（contextual bandits）的个性化方法，针对用户需求调整GAM配置。在包含108名用户的在线实验中，个性化组实现个体化模型配置，而对照组采用统一设置。尽管配置各异，两组用户均报告对模型有较强理解力，且可解释性水平保持高位。研究初步验证了个性化可解释机器学习的可行性。
原文摘要 · Abstract (English)
The Rashomon effect describes the observation that in machine learning (ML) multiple models often achieve similar predictive performance while explaining the underlying relationships in different ways. This observation holds even for intrinsically interpretable models, such as Generalized Additive Models (GAMs), which offer users valuable insights into the model's behavior. Given the existence of multiple GAM configurations with similar predictive performance, a natural question is whether we can personalize these configurations based on users' needs for interpretability. In our study, we developed an approach to personalize models based on contextual bandits. In an online experiment with 108 users in a personalized treatment and a non-personalized control group, we found that personalization led to individualized rather than one-size-fits-all configurations. Despite these individual adjustments, the interpretability remained high across both groups, with users reporting a strong understanding of the models. Our research offers initial insights into the potential for personalizing interpretable ML.
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