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
Teaching AI models to improve themselves
ResearchPeter HessIBM and RPI researchers demystify in-context learning in large language models
NewsPeter HessThe latest AI safety method is a throwback to our maritime past
ResearchKim MartineauFind and fix IT glitches before they crash the system
NewsKim MartineauWhat is retrieval-augmented generation?
ExplainerKim MartineauDid an AI write that? If so, which one? Introducing the new field of AI forensics
ExplainerKim Martineau- See more of our work on Explainable AI
Publications
- 2024
- AGU 2024
- Lloyd Treinish
- Mukul Tewari
- 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