One Explanation Does Not Fit All: A Toolkit And Taxonomy Of AI Explainability Techniques
- 2021
- INFORMS 2021
Samuel C. Hoffman is a research software engineer at the Thomas J. Watson Research Center in Yorktown Heights, NY. He graduated from Cornell University with a B.S. in mechanical engineering and computer science in 2017. While at Cornell, he conducted research in autonomous robotics and image recognition. He then joined IBM Research after graduating in 2017. His research interests include deep learning and generative modeling as well as algorithmic fairness in machine learning. He was part of the team that released the AI Fairness 360 open-source toolkit and has been a key contributor and maintainer of AIF360. He has also conducted recent research into foundation models for chemistry in the MoLFormers suite, having worked on CogMol, QMO, and MoLFormer-XL.