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
- Yiming Chen
- Niharika DSouza
- et al.
- 2024
- MICCAI 2024
- 2024
- BPM 2024
- Michal Muszynski
- Levente Klein
- et al.
- 2024
- KDD 2024
- Eunjeong Hwang
- Vered Shwartz
- et al.
- 2024
- ACL 2024
- Shreyas Basavatia
- Keerthiram Murugesan
- et al.
- 2024
- ACL 2024
- Oscar Sainz
- Iker García-ferrero
- et al.
- 2024
- ACL 2024