United States Government Publishing Office publisher pbl distributor dst United States Commerce Department National Institute of Standards and Technology (NIST) author aut Government Organization Schwartz, Reva. author aut text government publication eng GOVPUB Executive Agency Publications executive 2023-10-27 Commerce Department 2022-03-15 monographic deposited born digital 86 digital object pages C 13. https://www.govinfo.gov/app/details/GOVPUB-C13-9763c9cc5eda8562249fa6e860375a18 P0b002ee1c1bb3291 on1389890245 (OCoLC)1389890245 DGPO 2023-10-27 2026-06-27 GOVPUB-C13-9763c9cc5eda8562249fa6e860375a18 machine generated eng fdlp C13 GOVPUB-C13-9763c9cc5eda8562249fa6e860375a18 9763c9cc5eda8562249fa6e860375a18 GOVPUB NIST Special Publications NOTE: THE “DATE ISSUED” ABOVE MAY DEFAULT TO JANUARY 1ST OF A GIVEN YEAR. TO THE VIEW THE MOST ACCURATE DATE OF ISSUE, REVIEW THE TITLE PAGE OF THE PUBLICATION. This series includes proceedings of conferences sponsored by NIST, NIST annual reports, and other special publications appropriate to this grouping such as wall charts, pocket cards, and bibliographies. National Institute of Standards and Technology Commerce Department. National Institute of Standards and Technology. 2022 2022-03-15 https://www.govinfo.gov/app/details/GOVPUB-C13-9763c9cc5eda8562249fa6e860375a18 https://www.govinfo.gov/content/pkg/GOVPUB-C13-9763c9cc5eda8562249fa6e860375a18/pdf/GOVPUB-C13-9763c9cc5eda8562249fa6e860375a18.pdf AI Artificial intelligence Artificial intelligence bias Artificial intelligence development Artificial intelligence lifecycle Artificial intelligence safety Reliability Trustworthiness Schwartz, Reva. creator Schwartz, Reva. Vassilev, Apostol. Greene, Kristen. Perine, Lori. Burt, Andrew. Hall, Patrick. National Institute of Standards and Technology (U.S.) Engineering Laboratory mdu U.S. Dept. of Commerce, National Institute of Standards and Technology 2022-03-15. monographic 1 online resource (86 pages) : illustrations (color) text technical report eng As individuals and communities interact in and with an environment that is increasingly virtual they are often vulnerable to the commodification of their digital exhaust. Concepts and behavior that are ambiguous in nature are captured in this environment, quantified, and used to categorize, sort, recommend, or make decisions about people's lives. While many organizations seek to utilize this information in a responsible manner, biases remain endemic across technology processes and can lead to harmful impacts regardless of intent. These harmful outcomes, even if inadvertent, create significant challenges for cultivating public trust in artificial intelligence (AI). SP 1270 is a NIST Artificial Intelligence publication and should be read in conjunction with all publications in the NIST AI Series, which was established in January 2023. Reva Schwartz; Apostol Vassilev; Kristen Greene; Lori Perine; Andrew Burt; Patrick Hall. March 2022. Title from PDF title page (viewed January 4, 2023). Includes bibliographical references. Approved by the NIST Editorial Review Board on 2022-12-07 Mode of access: World Wide Web. Systems requirements: Adobe Acrobat PDF reader. Artificial intelligence Reliability Artificial intelligence bias AI Trustworthiness Artificial intelligence safety Artificial intelligence lifecycle Artificial intelligence development https://doi.org/10.6028/NIST.SP.1270