75 citations · 87 across the 11 of their papers we have counts for
11 papers
When AI Takes Sides on Questions of Faith: Persistent Asymmetries in AI-Mediated Faith Guidance
Brett Israelsen, Sheryl Carty, Josh Coates +3
We ask whether large language models (LLMs) treat queries about religious conversion symmetrically. The answer is no. When asked for advice on hypothetical faith transitions from r…
"A Good Bot Always Knows Its Limitations": Assessing Autonomous System Decision-making Competencies through Factorized Machine Self-confidence
Brett W. Israelsen, Nisar R. Ahmed, Matthew Aitken +3
How can intelligent machines assess their competency to complete a task? This question has come into focus for autonomous systems that algorithmically make decisions under uncertai…
A Factor-Based Framework for Decision-Making Competency Self-Assessment
Brett W. Israelsen, Nisar Ahmed
We summarize our efforts to date in developing a framework for generating succinct human-understandable competency self-assessments in terms of machine self confidence, i.e. a robo…
Evaluating Metrics for Standardized Benchmarking of Remote Presence Systems
Charles Peasley, Rachel Dianiska, Emily Oldham +5
To reduce the need for business-related air travel and its associated energy consumption and carbon footprint, the U.S. Department of Energy's ARPA-E is supporting a research proje…
Factorized Machine Self-Confidence for Decision-Making Agents
Brett W Israelsen, Nisar R Ahmed, Eric Frew +2
Algorithmic assurances from advanced autonomous systems assist human users in understanding, trusting, and using such systems appropriately. Designing these systems with the capaci…
"Dave...I can assure you...that it's going to be all right..." -- A definition, case for, and survey of algorithmic assurances in human-autonomy trust relationships
Brett W Israelsen, Nisar R Ahmed
People who design, use, and are affected by autonomous artificially intelligent agents want to be able to \emph{trust} such agents -- that is, to know that these agents will perfor…
"I can assure you [] that it's going to be all right" -- A definition, case for, and survey of algorithmic assurances in human-autonomy trust relationships
Brett W Israelsen
As technology become more advanced, those who design, use and are otherwise affected by it want to know that it will perform correctly, and understand why it does what it does, and…
Adaptive Simulation-based Training of AI Decision-makers using Bayesian Optimization
Brett W. Israelsen, Nisar Ahmed, Kenneth Center +2
This work studies how an AI-controlled dog-fighting agent with tunable decision-making parameters can learn to optimize performance against an intelligent adversary, as measured by…
Towards Adaptive Training of Agent-based Sparring Partners for Fighter Pilots
Brett W. Israelsen, Nisar Ahmed, Kenneth Center +2
A key requirement for the current generation of artificial decision-makers is that they should adapt well to changes in unexpected situations. This paper addresses the situation in…
Hybrid Repeat/Multi-point Sampling for Highly Volatile Objective Functions
Brett Israelsen, Nisar Ahmed
A key drawback of the current generation of artificial decision-makers is that they do not adapt well to changes in unexpected situations. This paper addresses the situation in whi…
Generalized Laguerre Reduction of the Volterra Kernel for Practical Identification of Nonlinear Dynamic Systems
Brett W. Israelsen, Dale A. Smith
The Volterra series can be used to model a large subset of nonlinear, dynamic systems. A major drawback is the number of coefficients required model such systems. In order to reduc…