United States Government Publishing Office publisher pbl distributor dst United States Commerce Department National Institute of Standards and Technology (NIST) author aut Government Organization Dadfarnia, Mehdi. author aut text government publication eng GOVPUB Executive Agency Publications executive 2023-10-27 Commerce Department 2022-09-30 monographic deposited born digital 19 digital object pages C 13. https://www.govinfo.gov/app/details/GOVPUB-C13-ef5a2fbb1a7a0c1b04a02d95af757970 P0b002ee1c1bb2d78 on1389890502 (OCoLC)1389890502 DGPO 2023-10-27 2026-06-27 GOVPUB-C13-ef5a2fbb1a7a0c1b04a02d95af757970 machine generated eng fdlp C13 GOVPUB-C13-ef5a2fbb1a7a0c1b04a02d95af757970 ef5a2fbb1a7a0c1b04a02d95af757970 GOVPUB NIST Interagency or Internal Reports 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. Interim or final reports on work performed by NIST for outside sponsors (both government and non-government). May also report results of NIST projects of transitory or limited interest, including those that will be published subsequently in more comprehensive form. National Institute of Standards and Technology Commerce Department. National Institute of Standards and Technology. 2022 2022-09-30 https://www.govinfo.gov/app/details/GOVPUB-C13-ef5a2fbb1a7a0c1b04a02d95af757970 https://www.govinfo.gov/content/pkg/GOVPUB-C13-ef5a2fbb1a7a0c1b04a02d95af757970/pdf/GOVPUB-C13-ef5a2fbb1a7a0c1b04a02d95af757970.pdf Risk Management & Industrial Artificial Intelligence, Informs Annual Meeting, 2021 (Virtual) Artificial intelligence Reliability (Engineering) Risk assessment Risk management Dadfarnia, Mehdi. creator Dadfarnia, Mehdi. Sharp, Michael. National Institute of Standards and Technology (U.S.) Material Measurement Laboratory mdu U.S. Dept. of Commerce, National Institute of Standards and Technology 2022-09-30. monographic 1 online resource (19 pages) : tables (black and white) text technical report eng Risk management is an important topic in any domain. With the growing acceptance and adoption of Industrial Artificial Intelligence (IAI), both the academic and industrial communities are beginning to take a more serious look at the risks and rewards of using IAI. This paper summarizes insights generated at the 2021 INFORMs panel addressing the risk-based evaluation of AI use from both a performance and business impacts perspective. The summarized insights are gathered and presented in a logical fashion to highlight identified gaps and barriers in the evaluation and adoption of IAI technologies. Additional context and suggestions regarding paths forward are provided by the authors. Mehdi Dadfarnia; Michael Sharp. September 2022. Title from PDF title page (viewed January 4, 2023). Includes bibliographical references. Approved by the NIST Editorial Review Board on 2022-09-20 Mode of access: World Wide Web. Systems requirements: Adobe Acrobat PDF reader. Artificial intelligence Reliability (Engineering) Risk assessment Risk management https://doi.org/10.6028/NIST.IR.8445 risk management & Industrial Artificial Intelligence, INFORMS annual meeting, 2021 (virtual) risk management & Industrial Artificial Intelligence, INFORMS annual meeting, 2021 (virtual)