3 citations · 11 across the 10 of their papers we have counts for
10 papers
Robustness and Usefulness in AI Explanation Methods
Erick Galinkin
Explainability in machine learning has become incredibly important as machine learning-powered systems become ubiquitous and both regulation and public sentiment begin to demand an…
Towards a Responsible AI Development Lifecycle: Lessons From Information Security
Erick Galinkin
Legislation and public sentiment throughout the world have promoted fairness metrics, explainability, and interpretability as prescriptions for the responsible development of ethic…
Who's Afraid of Thomas Bayes?
Erick Galinkin
In many cases, neural networks perform well on test data, but tend to overestimate their confidence on out-of-distribution data. This has led to adoption of Bayesian neural network…
The State of AI Ethics Report (January 2021)
Abhishek Gupta, Alexandrine Royer, Connor Wright +9
The 3rd edition of the Montreal AI Ethics Institute's The State of AI Ethics captures the most relevant developments in AI Ethics since October 2020. It aims to help anyone, from m…
The Influence of Dropout on Membership Inference in Differentially Private Models
Erick Galinkin
Differentially private models seek to protect the privacy of data the model is trained on, making it an important component of model security and privacy. At the same time, data sc…
Information Security Games: A Survey
Erick Galinkin
We introduce some preliminaries about game theory and information security. Then surveying a subset of the literature, we identify opportunities for future research.