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Cited by 20Human-Automation Interaction and SafetyInformation Systems Theories and ImplementationEthics and Social Impacts of AI

From Artificial Intelligence (AI) to Intelligence Augmentation (IA): Design Principles, Potential Risks, and Emerging Issues

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The University of North Carolina at Charlotte, Zhou, Lina, Rudin, Cynthia, Duke University, Gombolay, Matthew, Georgia Institute of Technology, Spohrer, Jim, International Society of Service Innovation Professionals +4 more·2023·AIS Transactions on Human-Computer Interaction
DOI 10.17705/1thci.00085
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Abstract

We typically think of artificial intelligence (AI) as focusing on empowering machines with human capabilities so that they can function on their own, but, in truth, much of AI focuses on intelligence augmentation (IA), which is to augment human capabilities. We propose a framework for designing intelligent augmentation (IA) systems and it addresses six central questions about IA: why, what, who/whom, how, when, and where. To address the how aspect, we introduce four guiding principles: simplification, interpretability, human-centeredness, and ethics. The what aspect includes an IA architecture that goes beyond the direct interactions between humans and machines by introducing their indirect relationships through data and domain. The architecture also points to the directions for operationalizing the IA design simplification principle. We further identify some potential risks and emerging issues in IA design and development to suggest new questions for future IA research and to foster its positive impact on humanity.

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  • Language models such as ChatGPT have been shown to reproduce stereotypes and narrow demographic representations.

    “Language models such as ChatGPT have been shown to reproduce stereotypes and narrow demographic representations, reinforcing the need for inclusive training practices and governance mechanisms (Gozalo-Brizuela & Garrido-Merchan, 2023;Zhou et al., 2023).”

    From Academic Integrity and Students’ Ethical Use of ChatGPT in Higher Education (2026) · cites this paper as Zhou et al., 2023

    Section 4.5, “Data Cycle in IA Development” (page not provided), says AI bias can involve demographic features and data representativeness, and calls for measures to mitigate it. The paper does not show that language models such as ChatGPT reproduce stereotypes or narrow demographic representations; its discussion of ChatGPT elsewhere concerns other risks. (LLM, conf=0.78)

    Experimental — accuracy not yet measured

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Research works printed but not linked: 24

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Authors
The University of North Carolina at Charlotte, Zhou, L., Rudin, C., Duke University, Gombolay, M., Georgia Institute of Technology, Spohrer, J., International Society of Service Innovation Professionals, Zhou, M., Juji, Paul, S., & Northern Kentucky University
Affiliations
  • University of North Carolina at Charlotte (US)
  • Duke University (US)
  • Georgia Institute of Technology (US)
  • International Society of Service Innovation Professionals (US)
  • Northern Kentucky University (US)
ORCID
Venue
AIS Transactions on Human-Computer Interaction
Publisher
Association for Information Systems
Published
2023-03-31
Volume
15
Issue
1
Pages
111-135
ISSN
1944-3900 (ISSN-L)
DOI
10.17705/1thci.00085
OpenAlex
W2594557664
Cited by
20
References
81
Citations by year
2025: 1 · 2024: 6 · 2023: 7 · 2022: 1 · 2020: 2 · 2019: 2 · 2018: 1
FWCI
1.96
Type
journal article
Language
en
Open access
Locations
  • AIS Transactions on Human-Computer Interaction (publishedVersion)
  • Journal of the Association for Information Systems (submittedVersion)
Funders
Eidgenössische Technische Hochschule Zürich
Topics
  • Social Sciences › Psychology › Social Psychology › Human-Automation Interaction and Safety
  • Social Sciences › Sociology and Political Science › Information Systems Theories and Implementation
  • Social Sciences › Safety Research › Ethics and Social Impacts of AI
Keywords
intelligence enhancement, artificial intelligence, human augmentation, simplification, interpretability, human-centered design, ethics, human-machine interaction

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