Explainable AI (original) (raw)

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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

• Teaching AI models to improve themselves

  • 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

• AI
• AI Transparency
• Explainable AI
• Trustworthy AI

• The latest AI safety method is a throwback to our maritime past

• AI
• AI Transparency
• Explainable AI
• Fairness, Accountability, Transparency
• Generative AI

• Find and fix IT glitches before they crash the system

• AI for Code
• AI for IT
• Explainable AI
• Foundation Models
• Generative AI

• What is retrieval-augmented generation?

• AI
• Explainable AI
• Generative AI
• Natural Language Processing
• Trustworthy Generation

• Did an AI write that? If so, which one? Introducing the new field of AI forensics

  • Adversarial Robustness and Privacy
  • AI
  • Explainable AI
  • Foundation Models
  • Generative AI
  • Trustworthy AI
• See more of our work on Explainable AI

Publications

• • • Daiki Kimura
      • Naomi Simumba
      • et al.
  • 2024
  • AGU 2024
• • • Radu Marinescu
      • Junkyu Lee
      • et al.
  • 2024
  • NeurIPS 2024
• • • Kumudu Geethan Karunaratne
      • Michael Hersche
      • et al.
  • 2024
  • NeurIPS 2024
• • • Lucas Monteiro Paes
      • Dennis Wei
      • et al.
  • 2024
  • NeurIPS 2024
• • • Somin Wadhwa
      • Oktie Hassanzadeh
      • et al.
  • 2024
  • ISWC 2024
• • • Lior Limonad
      • Fabiana Fournier
      • et al.
  • 2024
  • ECAI 2024

View all publications

Related topics

CausalityFairness, Accountability, TransparencyHealthcare and Life SciencesAI for Business Automation