AI Use Taxonomy: A Human-Centered Approach

Published

March 26, 2024

Author(s)

Mary Frances Theofanos, Yee-Yin Choong, Theodore Jensen

Abstract

As artificial intelligence (AI) systems continue to be developed, humans will increasingly participate in human-AI interactions. Humans interact with AI systems to achieve particular goals. To ensure that AI systems contribute positively to human-AI interactions, it is important to examine human-AI tasks with an emphasis on human goals and outcomes. The AI Use Taxonomy aims to provide a flexible means of classifying how an AI system contributes to an outcome. The taxonomy sets forward 16 AI use "activities" which are independent of AI techniques and domains. Tasks are combinations of one or more AI use activities. Future research includes applying the taxonomy to better understand measurement challenges for each activity. The taxonomy can contribute to an improved understanding of the architecture of human-AI tasks and help to foster positive, human-centered interactions with AI systems and optimal outcomes in the following ways: • Provides common terminology for describing outcome-based human-AI activities independent of AI techniques and domains • Enables cross-domain insights based on shared human-AI activities • Highlights commonalities in measurement and evaluation needs across disparate AI techniques • Facilitates the development of use cases • Facilitates the evaluation of trustworthiness characteristics and usability

Citation

NIST Trustworthy and Responsible AI - 200-1

Report Number

200-1

Pub Type

NIST Pubs

Download Paper

https://doi.org/10.6028/NIST.AI.200-1

Local Download

Keywords

Artificial intelligence, evaluation, human-centered AI, human-AI interaction, taxonomy, use case

Citation

Theofanos, M. , Choong, Y. and Jensen, T. (2024), AI Use Taxonomy: A Human-Centered Approach, NIST Trustworthy and Responsible AI, National Institute of Standards and Technology, Gaithersburg, MD, [online], https://doi.org/10.6028/NIST.AI.200-1, https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=956852 (Accessed October 27, 2025)