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
New Frontiers of Human-centered Explainable AI (HCXAI): Participatory Civic AI, Benchmarking LLMs and Hallucinations for XAI, and Responsible AI Audits
- Upol Ehsan
- Elizabeth Watkins
- et al.
- 2025
- CHI 2025
User centered approach of applicability domain analysis for improving AI-assisted decision-making
- Siya Kunde
- Emilio Ashton Vital Brazil
- 2025
- ACS Spring 2025
Extracting Electrolyte Design from Interpretable Data-Driven Methods
- 2025
- ACS Spring 2025
Leveraging Interpretability in the Transformer to Automate the Proactive Scaling of Cloud Resources
- Amadou Ba
- Pavithra Harsha
- et al.
- 2025
- AAAI 2025
Bridging the Gap Between AI Planning and Reinforcement Learning
- Zlatan Ajanovi{\'c}
- Timo Gros
- et al.
- 2025
- AAAI 2025
Neural Reasoning Networks: Efficient interpretable neural networks with automatic textual explanations
- Steve Carrow
- Kyle Harper Erwin
- et al.
- 2025
- AAAI 2025