219 citations · 227 across the 4 of their papers we have counts for
5 papers
Estimating the Brittleness of AI: Safety Integrity Levels and the Need for Testing Out-Of-Distribution Performance
Andrew J. Lohn
Test, Evaluation, Verification, and Validation (TEVV) for Artificial Intelligence (AI) is a challenge that threatens to limit the economic and societal rewards that AI researchers…
Toward Trustworthy AI Development: Mechanisms for Supporting Verifiable Claims
Miles Brundage, Shahar Avin, Jasmine Wang +56
With the recent wave of progress in artificial intelligence (AI) has come a growing awareness of the large-scale impacts of AI systems, and recognition that existing regulations an…
A Quantitative History of A.I. Research in the United States and China
Daniel Ish, Andrew Lohn, Christian Curriden
Motivated by recent interest in the status and consequences of competition between the U.S. and China in A.I. research, we analyze 60 years of abstract data scraped from Scopus to…
Adversarial Examples for Cost-Sensitive Classifiers
Gavin S. Hartnett, Andrew J. Lohn, Alexander P. Sedlack
Motivated by safety-critical classification problems, we investigate adversarial attacks against cost-sensitive classifiers. We use current state-of-the-art adversarially-resistant…
Defense in Depth: The Basics of Blockade and Delay
Andrew J. Lohn
Given that individual defenses are rarely sufficient, defense-in-depth is nearly universal and options for individual defensive layers abound. We develop a simple mathematical theo…