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
Leveraging Interpretability in the Transformer to Automate the Proactive Scaling of Cloud Resources
- Amadou Ba
- Pavithra Harsha
- et al.
- 2025
- AAAI 2025
Optimal Transport for Efficient, Unsupervised Anomaly Detection on Industrial Data
- Abigail Langbridge
- Fearghal O'Donncha
- et al.
- 2024
- Big Data 2024
Future Workload and Cloud Resource Usage: Insights from an Interpretable Forecasting Model
- 2024
- Big Data 2024
Abductive Reasoning in Logical Credal Networks
- Radu Marinescu
- Junkyu Lee
- et al.
- 2024
- NeurIPS 2024
On the role of noise in factorizers for disentangling distributed representations
- 2024
- NeurIPS 2024
Selective Explanations
- Lucas Monteiro Paes
- Dennis Wei
- et al.
- 2024
- NeurIPS 2024