Navigating the Rashomon Effect: How Personalization Can Help Adjust Interpretable Machine Learning Models to Individual Users

Abstract: The Rashomon effect describes the observation that in 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.

Personalized Interpretability in Machine Learning: Addressing the Rashomon Effect

The paper "Navigating the Rashomon Effect: How Personalization Can Help Adjust Interpretable Machine Learning Models to Individual Users" provides a rigorous analysis of personalization within the context of interpretable machine learning models. The authors focus on the Rashomon Effect, an observation inherent in machine learning whereby multiple models achieve similar predictive performance while explaining the data's internal relationships differently. Particularly, the study examines Generalized Additive Models (GAMs), a category of intrinsically interpretable models, to explore the feasibility of personalization based on users' specific needs for interpretability.

Overview of the Study and Methodological Approach

Machine learning models, particularly in critical areas like healthcare and criminal justice, necessitate interpretability to ensure transparency and accountability. Industries such as manufacturing and marketing similarly demand interpretability to bolster decision-making efficiency and user trust. Traditional interpretability approaches often adopt a one-size-fits-all mentality, which fails to address the diverse cognitive styles and preferences among users. This paper challenges this paradigm by presenting an approach to tailor interpretability using contextual bandits, leveraging the Rashomon Effect to provide personalized model configurations that accommodate individual users' interpretability needs while maintaining similar predictive performance.

The research involved an online experimental setup where 108 participants were divided into personalized treatment and non-personalized control groups. Participants acted as managers predicting bike-sharing demand with GAM-based models configured via contextual bandits, thus allowing evaluative feedback to influence the personalization strategy.

Key Findings and Numerical Results

The study reveals several key insights:

1. User Preferences and Model Configuration:
   • The research demonstrates that personalized models diverge significantly from conventional one-size-fits-all solutions. The study records a remarkable diversity, with 44 distinct GAM configurations resulting from 53 users in the treatment group, underscoring individual variances in interpretability needs.
2. Impact on Interpretability:
   • Despite personalization, the study found no significant differences affecting interpretability, measured via user perception and insight quality. Both personalized and control group models yielded comparable results on dimensions such as task complexity and cognitive effort.
3. Consistency in Predictive Performance:
   • GAMs, tailored for personalization and situated within the Rashomon set, exhibited consistent predictive prowess (R² ≥ 0.83) across varying configurations, ensuring performance was not compromised.

Theoretical and Practical Implications

The study holds significant implications for both theoretical exploration and practical deployment of interpretable machine learning systems. Personalized interpretability contributes to enhancing user-centricity in machine learning, thus fostering more informed decision-making. It signals a shift from assuming uniform user needs towards acknowledging the subjective nature of interpretability, paving the way for models that are more effective and trustworthy.

Moreover, findings suggest that organizations can implement such personalization without a trade-off in performance or interpretability quality. This offers valuable prospects for AI's integration into varied domains where user adaptability and model transparency are crucial.

Future Directions in AI

Future exploration should explore the scalability and wider applicability of personalized interpretability. Research should aim to extend this approach beyond GAMs into other intrinsically interpretable models and examine its efficacy across diverse datasets and domains. Moreover, it is vital to fine-tune feedback mechanisms within personalization frameworks to actively inform learning models beyond subjective user ratings.

In conclusion, this research advances a crucial discourse that underpins interpretable ML's evolution, highlighting personalized model configurations' pertinence in navigating cognitive variability among users. It disrupts the conventional interpretability assumptions, underscoring the importance of embracing model personalization while adhering to intrinsic fidelity.