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Search Publications by: Peter Fontana (Fed)

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Displaying 1 - 15 of 15

The Language of Trustworthy AI: An In-Depth Glossary of Terms

March 29, 2023
Author(s)
Daniel Atherton, Reva Schwartz, Peter Fontana, Patrick Hall
The NIST (National Institute of Standards and Technology) glossary of terms related to trustworthy and responsible artificial intelligence (AI) and machine learning (ML) intends to promote a common understanding and effective communication among

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.

Open Media Forensics Challenge (OpenMFC) 2020-2021: Past, Present, and Future

September 29, 2021
Author(s)
Haiying Guan, Yooyoung Lee, Lukas Diduch, Jesse Zhang, Ilia Ghorbanian Bajgiran, Timothee Kheyrkhah, Peter Fontana, Jonathan G. Fiscus
This document describes the online leaderboard public evaluation program, Open Media Forensics Challenge (OpenMFC) 2021-2022. In the report, first, the introduction, objectives, challenges, contributions, and achievements of the evaluation program are

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

A data science challenge for converting airborne remote sensing data into ecological information

February 28, 2019
Author(s)
Sergio Marconi, Sarah J. Graves, Dihong Gong, Shahriari Nia Morteza, Marion Le Bras, Bonnie J. Dorr, Peter Fontana, Justin Gearhart, Craig Greenberg, Dave J. Harris, Sugumar A. Kumar, Agarwal Nishant, Joshi Prarabdh, Sandeep U. Rege, Stephanie A. Bohlman, Ethan P. White, Daisy Z. Wang
In recent years ecology has reached the point where a data science competition could be very productive. Large amounts of open data are increasingly available and areas of shared interest around which to center competitions are increasingly prominent. The

NIST IAD DSE Evaluation Plan 2018

October 16, 2018
Author(s)
Bonnie J. Dorr, Peter Fontana, Craig Greenberg, Marion Le Bras, Maxime Hubert, Alexandre F. Boyer
This document describes the plan for the National Institute of Standards and Technology (NIST) Information Access Division (IAD) Data Science Evaluation (DSE) Series Evaluation to be held starting July 2018 (Tentative). The DSE consists of a series of

A New International Data Science Program

August 4, 2016
Author(s)
Bonnie J. Dorr, Craig Greenberg, Peter Fontana, Mark A. Przybocki, Marion Le Bras, Cathryn A. Ploehn, Oleg Aulov, Wo L. Chang
This article sets out to examine foundational issues in data science including current challenges, basic research questions, and expected advances, as the basis for a new Data Science Research Program and associated Data Science Evaluation (DSE) series

Data Science Research Program at NIST Information Access Division

August 4, 2016
Author(s)
Bonnie J. Dorr, Craig Greenberg, Peter Fontana, Mark A. Przybocki, Marion Le Bras, Cathryn A. Ploehn, Oleg Aulov, Edmond J. Golden III, Wo L. Chang
We examine foundational issues in data science including current challenges, basic research questions, and expected advances, as the basis for a new Data Science Initiative and evaluation series, introduced by the Information Access Division at the

The NIST IAD Data Science Evaluation Series: Part of the NIST Information Access Division Data Science Research Program

October 29, 2015
Author(s)
Bonnie J. Dorr, Craig Greenberg, Peter Fontana, Mark A. Przybocki, Marion Le Bras, Cathryn A. Ploehn, Oleg Aulov, Wo L. Chang
The Information Access Division (IAD) of the National Institute of Standards and Technology (NIST) launched a new Data Science Research Program (DSRP) in the fall of 2015. This research program focuses on evaluation-driven research and will establish a new

The NIST IAD Data Science Research Program

October 19, 2015
Author(s)
Bonnie J. Dorr, Peter C. Fontana, Craig S. Greenberg, Mark A. Przybocki, Marion Le Bras, Cathryn A. Ploehn, Oleg Aulov, Martial Michel, Edmond J. Golden III, Wo L. Chang
We examine foundational issues in data science including current challenges, basic research questions, and expected advances, as the basis for a new Data Science Research Program and evaluation series, introduced by the Information Access Division of the