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Explainable AI

To trust AI systems, explanations can go a long way. We’re creating tools to help debug AI, where systems can explain what they’re doing. This includes training highly optimized, directly interpretable models, as well as explanations of black-box models and visualizations of neural network information flows.

Our work

  • How the wrong training environment can teach AI models to misbehave

    Research
    Peter Hess
    09 Jul 2026
    • AI
    • AI Planning
    • Explainable AI
    • Generative AI
  • Debugging LLMs to improve their credibility

    Research
    Kim Martineau
    30 Jul 2025
    • AI
    • Explainable AI
    • Generative AI
    • Natural Language Processing
    • Open Source
    • Trustworthy AI
    • Trustworthy Generation
  • Teaching AI models to improve themselves

    IBM Research scientists are working on different approaches to teach AI models how to improve themselves
    Research
    Peter Hess
    14 Aug 2024
    • AI
    • Computer Science
    • Explainable AI
    • Generative AI
    • Natural Language Processing
    • Trustworthy AI
    • Trustworthy Generation
  • IBM and RPI researchers demystify in-context learning in large language models

    News
    Peter Hess
    25 Jul 2024
    • AI
    • AI Transparency
    • Explainable AI
    • Trustworthy AI
  • The latest AI safety method is a throwback to our maritime past

    Research
    Kim Martineau
    16 Nov 2023
    • AI
    • AI Transparency
    • Explainable AI
    • Fairness, Accountability, Transparency
    • Generative AI
  • Find and fix IT glitches before they crash the system

    News
    Kim Martineau
    28 Sep 2023
    • AI for Code
    • AI for IT
    • Explainable AI
    • Foundation Models
    • Generative AI
  • See more of our work on Explainable AI

Related topics

  • Causality
  • Fairness, Accountability, Transparency
  • Healthcare and Life Sciences
  • AI for Business Automation
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