Uncertainty Quantification
When AI can explain to us that it's unsure, it adds a critical layer of transparency for its safe deployment and use. We’re developing ways to foster and streamline the common practices of quantifying, evaluating, improving, and communicating uncertainty in the AI application development lifecycle.
Our work
IBM’s Uncertainty Quantification 360 toolkit boosts trust in AI
ReleasePrasanna Sattigeri and Vera Liao7 minute read- Human-Centered AI
- Trustworthy AI
- Uncertainty Quantification
AI boosts the discovery of metamaterials vital for next-gen gadgets
ResearchYoussef Mroueh, Karthikeyan Shanmugam, and Payel Das10 minute read- AI
- Materials Discovery
- Trustworthy Generation
- Uncertainty Quantification
Publications
- Nicolas Deutschmann
- Marvin Alberts
- et al.
- 2024
- AAAI 2024
- Thomas Frick
- Diego Antognini
- et al.
- 2024
- TMLR
- Felipe Maia Polo
- Mikhail Yurochkin
- et al.
- 2023
- NeurIPS 2023
- Robert Alison
- Anthony Stephenson
- et al.
- 2023
- NeurIPS 2023
- Tim Rumbell
- Catherine Wanjiru
- et al.
- 2023
- NeurIPS 2023
- Ella Rabinovich
- Samuel Ackerman
- et al.
- 2023
- EMNLP 2023