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ChatGPT: More Than a “Weapon of Mass Deception” Ethical Challenges and Responses from the Human-Centered Artificial Intelligence (HCAI) Perspective
Open accessPublished version of a preprint
Abstract
This article explores the ethical problems arising from the use of ChatGPT as a kind of generative AI and suggests responses based on the Human-Centered Artificial Intelligence (HCAI) framework. The HCAI framework is appropriate because it understands technology above all as a tool to empower, augment, and enhance human agency while referring to human wellbeing as a “grand challenge”, thus perfectly aligning itself with ethics, the science of human flourishing. Further, HCAI provides objectives, principles, procedures, and structures for reliable, safe, and trustworthy AI which we apply to our ChatGPT assessments. The main danger ChatGPT presents is the propensity to be used as a “weapon of mass deception” (WMD) and an enabler of criminal activities involving deceit. We review technical specifications to better comprehend its potentials and limitations. We then suggest both technical (watermarking, styleme, detectors, and fact-checkers) and non-technical measures (terms of use, transparency, educator considerations, HITL) to mitigate ChatGPT misuse or abuse and recommend best uses (creative writing, non-creative writing, teaching and learning). We conclude with considerations regarding the role of humans in ensuring the proper use of ChatGPT for individual and social wellbeing. This is an original manuscript of an article published by Taylor & Francis in the International Journal of Human-Computer Interaction on June 27th, 2023, available online: https://doi.org/10.1080/10447318.2023.2225931
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“Likewise, the perceived risk of encountering hallucinated outputs can lead users to fact-check content or rely on AI detection tools (Sison et al. 2024).”
From Bridging risk and retention: risk coping with privacy and hallucination in AI (2026) · cites this paper as Sison et al. 2024
In the citing paper
“This pattern is consistent with Sison et al. (2024), who argue that awareness of AI limitations can motivate users to adopt verification practices as a way of managing uncertainty, rather than disengaging from AI systems altogether.”
From Bridging risk and retention: risk coping with privacy and hallucination in AI (2026) · cites this paper as Sison et al. (2024)
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Metadata
- Authors
- Sison, A. J. G., Daza, M. T., Gozalo-Brizuela, R., & Garrido-Merchán, E. C.
- Affiliations
- School of Economics and Business Administration, Institute of Data Science and Artificial Intelligence (DATAI), Universidad de Navarra
- Universidad de Guadalajara (MX)
- Universidad Pontificia Comillas (ES)
- Quantitative Methods Department, Universidad Pontificia Comillas
- Corresponding author
- Daza, Marco Tulio
- Venue
- International Journal of Human–Computer Interaction
- Publisher
- Informa UK Limited
- Published
- 2023-06-27
- Volume
- 40
- Issue
- 17
- Pages
- 4853-4872
- ISSN
- 1044-7318 (ISSN-L)
- 1532-7590
- OpenAlex
- W4382249028
- Cited by
- 169
- References
- 56
- Citations by year
- 2026: 41 · 2025: 60 · 2024: 53 · 2023: 15
- FWCI
- 42.32
- Type
- journal article
- Language
- en
- Open access
- green
- doi.org
- Locations
- International Journal of Human-Computer Interaction (publishedVersion)
- SSRN Electronic Journal (acceptedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Ethics and Social Impacts of AI
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Physical Sciences › Computer Science › Artificial Intelligence › Artificial Intelligence Applications
- Keywords
- ChatGPT, generative AI ethics, human-centered AI, AI deception, watermarking, AI detection, fact-checking, trustworthy AI, human agency, human well-being, human-in-the-loop, artificial intelligence in education, transparency, terms of use
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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