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Search Publications by: Carina Hahn (Fed)

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Displaying 1 - 7 of 7

Who Is That? Perceptual Expertise on Other-Race Face Comparisons, Disguised Face Comparisons, and Face Memory

April 20, 2023
Author(s)
Amy Yates, Jacqueline Cavazos, Geraldine Jeckeln, Ying Hu, Eilidh Noyes, Carina Hahn, Alice O'Toole, P. Jonathon Phillips
Forensic facial specialists identify faces more accurately than untrained participants on tests using high quality images of faces. Whether this superiority holds in more challenging conditions is not known. Here, we measured performance for forensic

NIST Explainable AI Workshop Summary

August 25, 2022
Author(s)
P. Jonathon Phillips, Carina Hahn, Peter Fontana, Amy Yates, Matthew Smith
This report represents a summary of the National Institute of Standards and Technology (NIST) Explainable Artificial Intelligence (AI) Workshop, which NIST held virtually on January 26-28, 2021.

Forensic facial examiners vs. super-recognizers: Evaluating behavior beyond accuracy

August 24, 2021
Author(s)
Carina Hahn, Liansheng Larry Tang, Amy Yates, P. Jonathon Phillips
Forensic facial examiners and super-recognizers are highly accurate face matchers and outperform the general population. Typically, forensic facial examiners are highly trained, whereas super-recognizers are thought to rely on natural ability. Previous

Four Principles of Explainable Artificial Intelligence (Draft)

August 18, 2020
Author(s)
P J. Phillips, Amanda C. Hahn, Peter C. Fontana, David A. Broniatowski, Mark A. Przybocki
We introduce four principles for explainable artificial intelligence (AI) that comprise the fundamental properties for explainable AI systems. They were developed to encompass the multidisciplinary nature of explainable AI, including the fields of computer