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Towards a Standard for Identifying and Managing Bias in Artificial Intelligence

Published

Author(s)

Reva Schwartz, Apostol Vassilev, Kristen K. Greene, Lori Perine, Andrew Burt, Patrick Hall

Abstract

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).
Citation
Special Publication (NIST SP) - 1270
Report Number
1270

Keywords

bias, trustworthiness, AI safety, AI lifecycle, AI development

Citation

Schwartz, R. , Vassilev, A. , Greene, K. , Perine, L. , Burt, A. and Hall, P. (2022), Towards a Standard for Identifying and Managing Bias in Artificial Intelligence, Special Publication (NIST SP), National Institute of Standards and Technology, Gaithersburg, MD, [online], https://doi.org/10.6028/NIST.SP.1270, https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=934464 (Accessed September 23, 2022)
Created March 15, 2022