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Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems

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XAI-Explainable artificial intelligence.

David Gunning1, Mark Stefik2, Jaesik Choi3

  • 1Defense Advanced Research Projects Agency (DARPA), 675 North Randolph Street, Arlington, VA 22201, USA. dgunning@fb.com gzyang@sjtu.edu.cn.

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Summary
This summary is machine-generated.

Explainable artificial intelligence (AI) is crucial for users to understand and trust AI systems. This ensures effective management of advanced AI applications.

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Human-Computer Interaction

Background:

  • The increasing prevalence of artificial intelligence (AI) necessitates methods for user comprehension.
  • Lack of transparency in AI decision-making hinders user trust and adoption.

Purpose of the Study:

  • To highlight the critical role of explainability in AI.
  • To underscore the importance of user understanding, trust, and effective management of AI.

Main Methods:

  • Conceptual analysis of AI explainability principles.
  • Review of existing literature on AI trust and user interaction.

Main Results:

  • Explainability directly correlates with user trust in AI systems.
  • Understandable AI fosters effective user control and management.

Conclusions:

  • Implementing explainable AI (XAI) is paramount for successful AI deployment.
  • User-centric AI design must prioritize transparency and interpretability.