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Related Experiment Video

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Assessing Model Requirements for Explainable AI: A Template and Exemplary Case Study.

Michael Heider1, Helena Stegherr2, Richard Nordsieck3

  • 1Universität Augsburg, Organic Computing Group. michael.heider@uni-a.de.

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|August 23, 2023
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Summary

Decision support systems (DSS) in sociotechnical settings can improve transparency using learning classifier systems (LCSs). A novel approach using seven questions aids in designing LCS models for better human-AI interaction.

Keywords:
Rule-based learningdecision supportexplainable AIlearning classifier systemself-explainingsociotechnical system

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Area of Science:

  • Sociotechnical Systems
  • Artificial Intelligence
  • Human-Computer Interaction

Background:

  • Human operators increasingly use decision support systems (DSS) in sociotechnical settings.
  • Effective DSS require transparent decision-making for operator acceptance and engagement.
  • Self-adaptation and self-optimization are key properties expected to improve with DSS.

Purpose of the Study:

  • To propose learning classifier systems (LCSs) for transparent decision-making in DSS.
  • To introduce a novel approach for assessing application-specific explainability needs in LCS design.
  • To provide a template of seven questions for guiding LCS model development.

Main Methods:

  • Utilizing learning classifier systems (LCSs), a family of rule-based machine learning methods.
  • Developing an application-independent template of seven questions to assess explainability needs.
  • Conducting an interview-based case study in a manufacturing scenario.

Main Results:

  • The proposed seven-question template yields useful insights for LCS model design.
  • Stakeholder requirements for active engagement with intelligent agents were identified.
  • The approach facilitates the design of transparent and explainable LCS models.

Conclusions:

  • Learning classifier systems (LCSs) offer a viable method for enhancing transparency in decision support systems.
  • A structured approach to assessing explainability needs is crucial for effective LCS design.
  • Improved transparency in LCS fosters better collaboration between human operators and intelligent agents.