Daily Note: Tangible Interfaces in Explainable AI

These notes are a summary of concepts presented in “Exploring Tangible Explainable AI (TangXAI): A User Study of Two XAI Approaches.”

Ashley Colley, Matilda Kalving, Jonna Häkkilä, and Kaisa Väänänen. 2024. Exploring Tangible Explainable AI (TangXAI): A User Study of Two XAI Approaches. In Proceedings of the 35th Australian Computer-Human Interaction Conference (OzCHI ’23). Association for Computing Machinery, New York, NY, USA, 679–683. https://doi.org/10.1145/3638380.3638426

  1. . Overview of Explainable AI Approaches
    • Highlight input parameters most significant to the AI’s decision (feature relevance)
    • Explain how input parameters must shift to alter AI conclusions (local explanations)
  2. Concept of Data Physicalization
    • Translating digital data into tangible, physical forms (e.g., 3D printed models)
    • Aims to make abstract AI concepts more tangible and understandable
  3. Tangible Interaction Design for Explainable AI
    • Enables physical interaction with AI explanations
    • Supports collaborative data exploration and deeper understanding
  4. General XAI Approaches
    • Simplified rule extraction
      • Breaking AI logic into simple rules
    • Feature relevance
      • Scoring the importance of input parameters
    • Local explanations
      • Highlighting parameter changes needed to alter AI outcomes
    • Visual explanations
      • Using visual representations to convey AI behavior
  5. Tangible Interfaces for Feature Relevance and Local Explanations
    • Feature relevance
      • Scores parameter importance in AI decisions
      • Helps identify critical and irrelevant parameters
    • Local explanations
      • Demonstrates minimum input changes to shift AI decisions
  6. Case Study: Tangible Interfaces in Action
    • Lego Duplo bar chart
      • Visualizes parameters driving AI recipe recommendations
      • Users adjust recommendations by adding/removing bricks
    • Parameter expansion
      • Encourages users to suggest additional relevant parameters
  7. Insights on Trust and Explainable AI Interaction
    • Discussions on trust emphasized training data accuracy and parameter selection
    • Experimental trust-building through comparisons (e.g., route planning tools)
    • AI model performance not explicitly linked to trust perceptions
  8. Challenges in Understanding Explainable AI Interfaces
    • Confusion between using the AI tool and the Explainable AI interface for understanding
    • Feature relevance
      • Simple physical representations (e.g., Lego blocks) appreciated
      • Misinterpretation as a selection tool interface
    • Local explanations
      • Less comprehensible, needing alternative design approaches
  9. Impact of Tangible Interfaces
    • Slower interactions promote deeper understanding
    • Enhanced trust and comprehension but less suited for
      • High-speed interactions
      • Scalability to numerous parameters
      • Portability
  10. Future Considerations
    • Balance between tangible and digital interfaces based on use-case requirements
    • Address misunderstandings and usability issues in local explanation designs
    • Evaluate scalability and speed trade-offs in tangible Explainable AI applications