Publications

935 results for Trustworthy AI

One Explanation Does Not Fit All: A Toolkit And Taxonomy Of AI Explainability Techniques
- - Vijay Arya
  - Rachel K. Bellamy
  - et al.
- 2021
- INFORMS 2021
Talk
Addressing the Design Needs of Implementing Fairness in AI via Influence Functions
- - Brianna Richardson
  - Kush R. Varshney
- 2021
- INFORMS 2021
Talk
Robust Feature Based Model Agnostic Explainability For Time Series Forecasting
- - Vikas Raykar
  - Sumanta Mukherjee
  - et al.
- 2021
- INFORMS 2021
Talk
Explainability For Efficient And Trusted Decision Optimization
- - Kanthi Sarpatwar
  - Surya Shravan Kumar Sajja
  - et al.
- 2021
- INFORMS 2021
Talk
Privacy preserving explanations for hierarchical time series forecasts
- - Surya Shravan Kumar Sajja
  - Sumanta Mukherjee
  - et al.
- 2021
- INFORMS 2021
Talk
Detecting model drift using polynomial relations
- - Eliran Roffe
  - Samuel Ackerman
  - et al.
- 2022
- AAAI 2022
Workshop paper
The Combinatorial Generation of Objective Targets and Constraints for Large-scale Testing of Optimization Routines
- - Orit Davidovich
- 2021
- FWCG 2021
Workshop paper
Secure Random Sampling in Differential Privacy
- - Naoise Holohan
  - Stefano Braghin
- 2021
- ESORICS 2021
Conference paper
Anonymizing Machine Learning Models
- - Abigail Goldsteen
  - Micha Moffie
  - et al.
- 2021
- ESORICS 2021
Workshop paper
Dynamic graph and polynomial chaos based models for contact tracing data analysis and optimal testing prescription
- - Shashanka Ubaru
  - Lior Horesh
  - et al.
- 2021
- Journal of Biomedical Informatics
Paper