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Updated: Aug 7, 2025

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems
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Leveraging explanations in interactive machine learning: An overview.

Stefano Teso1, Öznur Alkan2, Wolfgang Stammer3

  • 1CIMeC and DISI, University of Trento, Trento, Italy.

Frontiers in Artificial Intelligence
|March 13, 2023
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Summary
This summary is machine-generated.

This study explores interactive explanations in Artificial Intelligence (AI) and Machine Learning (ML). Combining explanations with user interaction aids in learning new models and debugging existing ones.

Keywords:
explainable AIhuman-in-the-loopinteractive machine learningmodel debuggingmodel editing

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Area of Science:

  • Artificial Intelligence
  • Machine Learning
  • Human-Computer Interaction

Background:

  • Explanations are crucial for AI and ML model transparency and user understanding.
  • Current explanations often function as one-way communication, limiting user input.
  • There's a growing need to leverage explanations for active user engagement and control.

Purpose of the Study:

  • To provide an overview of research combining AI/ML explanations with interactive capabilities.
  • To explore how interactive explanations can facilitate learning new models from scratch.
  • To investigate the use of interactive explanations for editing and debugging existing AI/ML models.

Main Methods:

  • Conceptual mapping of the state-of-the-art in interactive explanations.
  • Categorization of approaches based on their purpose and interaction structure.
  • Analysis of similarities and differences among existing research.

Main Results:

  • A conceptual map illustrating the landscape of interactive explanation research.
  • Identification of distinct categories of interactive explanation systems.
  • Highlighting of commonalities and divergences in research methodologies.

Conclusions:

  • Interactive explanations offer a powerful mechanism for bidirectional communication in AI/ML.
  • This approach can enhance model development through user feedback and control.
  • Further research is needed to explore open issues and future directions in this field.