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The grammar of interactive explanatory model analysis.

Hubert Baniecki1,2, Dariusz Parzych1, Przemyslaw Biecek1,2

  • 1Warsaw University of Technology, Warsaw, Poland.

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Summary

Explaining machine learning models requires multiple methods to avoid the Rashomon effect. Interactive Explanatory Model Analysis (IEMA) combines different techniques for a comprehensive understanding.

Keywords:
Black-box modelExplainable AIHuman-centered XAIInteractive explainabilityModel-agnostic explanation

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Area of Science:

  • Machine Learning
  • Explainable AI (XAI)
  • Cognitive Science

Background:

  • Predictive models require in-depth analysis for local and global property explanation.
  • Single explanation methods for black-box models are insufficient, leading to the Rashomon effect.
  • Current explainable AI methods often focus on single aspects, risking misunderstanding.

Purpose of the Study:

  • To address the limitations of single-method explanations in machine learning.
  • To propose Interactive Explanatory Model Analysis (IEMA) as a solution to the Rashomon effect.
  • To formalize the grammar of IEMA for human-model interaction.

Main Methods:

  • Developing Interactive Explanatory Model Analysis (IEMA) by integrating diverse EMA methods.
  • Formalizing a grammar for IEMA to describe human-model interaction.
  • Implementing IEMA in an open-source, human-centered software framework.

Main Results:

  • Demonstrated how different EMA methods complement each other through juxtaposition.
  • Showcased IEMA as an interactive and sequential analysis process.
  • User study indicated IEMA enhances accuracy and confidence in human decision-making.

Conclusions:

  • A single explanation method is insufficient for understanding complex machine learning models.
  • Interactive EMA (IEMA) provides a more robust and comprehensive approach to model explainability.
  • IEMA integrates algorithmic insights with cognitive science principles for improved human-AI interaction.