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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)
Citations
Cites
LeCun, Y., Bengio, Y., & Hinton, G. (2015) · Nature · doi:10.1038/nature14539 · Cited by 84,126
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2018) · arXiv · doi:10.48550/arxiv.1810.04805 · Cited by 73,173
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
Raffel, C., Shazeer, N., Roberts, A., Lee, K., Narang, S., Matena, M., Zhou, Y., Li, W., & Liu, P. J. (2019) · arXiv · doi:10.48550/arxiv.1910.10683 · Cited by 10,929
On the Dangers of Stochastic Parrots: Can Language Models Be Too Big? 🦜
Bender, E. M., Gebru, T., McMillan-Major, A., & Shmitchell, S. (2021) · Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency · doi:10.1145/3442188.3445922 · Cited by 6,988 · cited 1×
“It has also been called a “ stochastic parrot, ” randomly stitching words together without reference or meaning (Bender et al., 2021).”
Dwivedi, Y. K., Kshetri, N., Hughes, L., Slade, E. L., Jeyaraj, A., Kar, A. K., Baabdullah, A. M., Koohang, A., Raghavan, V., Ahuja, M., et al. (2023) · International Journal of Information Management · doi:10.1016/j.ijinfomgt.2023.102642 · Cited by 4,584
ELIZA—a computer program for the study of natural language communication between man and machine
Weizenbaum, J. (1966) · Communications of the ACM · doi:10.1145/365153.365168 · Cited by 4,452
Kung, T. H., Cheatham, M., Medenilla, A., Sillos, C., De Leon, L., Elepaño, C., Madriaga, M., Aggabao, R., Diaz-Candido, G., Maningo, J., et al. (2023) · PLOS Digital Health · doi:10.1371/journal.pdig.0000198 · Cited by 4,115
Experimental evidence of massive-scale emotional contagion through social networks
Kramer, A. D. I., Guillory, J. E., & Hancock, J. T. (2014) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1320040111 · Cited by 3,687
ELIZA — a computer program for the study of natural language communication between man and machine
Weizenbaum, J. (1983) · Communications of the ACM · doi:10.1145/357980.357991 · Cited by 2,807
What if the devil is my guardian angel: ChatGPT as a case study of using chatbots in education
Tlili, A., Shehata, B., Adarkwah, M. A., Bozkurt, A., Hickey, D. T., Huang, R., & Agyemang, B. (2023) · Smart Learning Environments · doi:10.1186/s40561-023-00237-x · Cited by 2,003 · cited 3×
“Generally, it ’ s better to be adaptive, focusing on the benefits technological innovation brings to education rather than resisting change, embracing AI instead of proscribing it (Mucharraz y Cano et al., 2023; Tlili et al., 2023).”
Human-Centered Artificial Intelligence: Reliable, Safe & Trustworthy
Shneiderman, B. (2020) · International Journal of Human–Computer Interaction · doi:10.1080/10447318.2020.1741118 · Cited by 1,743
Lim, W. M., Gunasekara, A., Pallant, J. L., Pallant, J. I., & Pechenkina, E. (2023) · The International Journal of Management Education · doi:10.1016/j.ijme.2023.100790 · Cited by 1,261
Shneiderman, B. (2022) · doi:10.1093/oso/9780192845290.001.0001 · Cited by 1,185
Deep reinforcement learning from human preferences
Christiano, P., Leike, J., Brown, T. B., Martic, M., Legg, S., & Amodei, D. (2017) · arXiv · doi:10.48550/arxiv.1706.03741 · Cited by 1,058
Lund, B. D., Wang, T., Mannuru, N. R., Nie, B., Shimray, S., & Wang, Z. (2023) · Journal of the Association for Information Science and Technology · doi:10.1002/asi.24750 · Cited by 919
ChatGPT listed as author on research papers: many scientists disapprove
Stokel-Walker, C. (2023) · Nature · doi:10.1038/d41586-023-00107-z · Cited by 868
Can artificial intelligence help for scientific writing?
Salvagno, M., Taccone, F. S., & Gerli, A. G. (2023) · Critical Care · doi:10.1186/s13054-023-04380-2 · Cited by 827
Chatbots in Education and Research: A Critical Examination of Ethical Implications and Solutions
Kooli, C. (2023) · Sustainability · doi:10.3390/su15075614 · Cited by 666 · cited 7×
“Similarly, ChatGPT has lowered the marginal costs of producing new and original human-sounding texts to practically zero (once the costs of construction, training, maintenance, and so forth are covered) (Klein, 2023) leading to greater efficiency (Kooli, 2023).”
InfluentialA Systematic Literature Review of User Trust in AI-Enabled Systems: An HCI Perspective
Bach, T. A., Khan, A. N., Hallock, H., Beltrão, G., & Sousa, S. (2022) · International Journal of Human-Computer Interaction · doi:10.1080/10447318.2022.2138826 · Cited by 506
Six Human-Centered Artificial Intelligence Grand Challenges
Ozmen Garibay, O., Winslow, B., Andolina, S., Antona, M., Bodenschatz, A., Coursaris, C., Falco, G., Fiore, S. M., Garibay, I., Grieman, K., et al. (2023) · International Journal of Human–Computer Interaction · doi:10.1080/10447318.2022.2153320 · Cited by 501
Solove, D. J. (2002) · California Law Review · doi:10.2307/3481326 · Cited by 491
Harrer, S. (2023) · eBioMedicine · doi:10.1016/j.ebiom.2023.104512 · Cited by 480
Human-Centered Artificial Intelligence: Three Fresh Ideas
Shneiderman, B. (2020) · AIS Transactions on Human-Computer Interaction · doi:10.17705/1thci.00131 · Cited by 430
Sowa, K., Przegalinska, A. K., & Ciechanowski, L. (2021) · Journal of Business Research · doi:10.1016/j.jbusres.2020.11.038 · Cited by 259
ChatGPT is not all you need. A state-of-the-art review of large generative AI models
Gozalo-Brizuela, R., & Garrido-Merchan, E. C. (2023) · arXiv · doi:10.48550/arxiv.2301.04655 · Cited by 245
Self-citationSong, S. W., & Shin, M. (2024) · International Journal of Human–Computer Interaction · doi:10.1080/10447318.2022.2121038 · Cited by 244
Empowering learners for the age of artificial intelligence
Gašević, D., Siemens, G., & Sadiq, S. (2023) · Computers and Education: Artificial Intelligence · doi:10.1016/j.caeai.2023.100130 · Cited by 234
Is using ChatGPT cheating, plagiarism, both, neither, or forward thinking?
Anders, B. A. (2023) · Patterns · doi:10.1016/j.patter.2023.100694 · Cited by 192
Bommarito, M. J., & Katz, D. M. (2022) · doi:10.2139/ssrn.4314839 · Cited by 181
New Modes of Learning Enabled by AI Chatbots: Three Methods and Assignments
Mollick, E. R., & Mollick, L. (2022) · SSRN Electronic Journal · doi:10.2139/ssrn.4300783 · Cited by 176 · cited 1×
“Nonetheless, at university level instruction, ChatGPT could be useful in stimulating critical thinking, through output evaluations or assessments (Mollick and Mollick, 2022).”
ChatGPT and Publication Ethics
Rahimi, F., & Talebi Bezmin Abadi, A. (2023) · Archives of Medical Research · doi:10.1016/j.arcmed.2023.03.004 · Cited by 158
DetectGPT: Zero-Shot Machine-Generated Text Detection using Probability Curvature
Mitchell, E., Lee, Y., Khazatsky, A., Manning, C. D., & Finn, C. (2023) · arXiv · doi:10.48550/arxiv.2301.11305 · Cited by 151
ChatGPT and the Future of Academic Integrity in the Artificial Intelligence Era: A New Frontier
Mijwil, M. M., Hiran, K. K., Doshi, R., Dadhich, M., Al-Mistarehi, A.-H. W., & Bala, I. (2023) · Al-Salam Journal for Engineering and Technology · doi:10.55145/ajest.2023.02.02.015 · Cited by 149
To ChatGPT or not to ChatGPT? The Impact of Artificial Intelligence on Academic Publishing
Curtis, N. C., & ChatGPT (2023) · The Pediatric Infectious Disease Journal · doi:10.1097/inf.0000000000003852 · Cited by 138
Design Lessons From AI’s Two Grand Goals: Human Emulation and Useful Applications
Shneiderman, B. (2020) · IEEE Transactions on Technology and Society · doi:10.1109/tts.2020.2992669 · Cited by 128
Towards Making the Most of ChatGPT for Machine TranslationPreprint
Peng, K., Ding, L., Zhong, Q., Shen, L., Liu, X., Zhang, M., Ouyang, Y., & Tao, D. (2023) · SSRN Electronic Journal · doi:10.2139/ssrn.4390455 · Cited by 112
Mijwil, M. M., Aljanabi, M., & ChatGPT (2023) · Iraqi Journal for Computer Science and Mathematics · doi:10.52866/ijcsm.2023.01.01.0019 · Cited by 111
ChatGPT in Research: Balancing Ethics, Transparency and Advancement
Graf, A., & Bernardi, R. E. (2023) · Neuroscience · doi:10.1016/j.neuroscience.2023.02.008 · Cited by 98
The long road to fairer algorithms
Kusner, M. J., & Loftus, J. R. (2020) · Nature · doi:10.1038/d41586-020-00274-3 · Cited by 94
Shneiderman, B. (2021) · Communications of the ACM · doi:10.1145/3445973 · Cited by 84
da Silva, J. A. T. (2023) · Nurse Education in Practice · doi:10.1016/j.nepr.2023.103600 · Cited by 82
Predictive Entropy Search for Multi-objective Bayesian Optimization with Constraints
Garrido‐Merchán, E. C., & Hernández-Lobato, D. (2019) · Neurocomputing · doi:10.1016/j.neucom.2019.06.025 · Cited by 76
Self-citationArtificial intelligence and human rights: a business ethical assessment
KRIEBITZ, A., & LÜTGE, C. (2020) · Business and Human Rights Journal · doi:10.1017/bhj.2019.28 · Cited by 76
Lu, Q., Qiu, B., Ding, L., Xie, L., & Tao, D. (2023) · Preprints.org · doi:10.20944/preprints202303.0255.v1 · Cited by 74
ThoughtSource: A central hub for large language model reasoning data
Ott, S., Hebenstreit, K., Liévin, V., Hother, C. E., Moradi, M., Mayrhauser, M., Praas, R., Winther, O., & Samwald, M. (2023) · Scientific Data · doi:10.1038/s41597-023-02433-3 · Cited by 66
Artificial Intelligence in Education - a Promise, a Threat or a Hype?
Humble, N., & Mozelius, P. (2019) · DiVA (University of Gävle) · doi:10.34190/eciair.19.005 · Cited by 59
A Sociological Conversation with ChatGPT about AI Ethics, Affect and Reflexivity
Balmer, A. (2023) · Sociology · doi:10.1177/00380385231169676 · Cited by 51
(2023) · Nature Machine Intelligence · doi:10.1038/s42256-023-00613-9 · Cited by 45
Can an artificial intelligence chatbot be the author of a scholarly article?
Lee, J. Y. (2023) · Science Editing · doi:10.6087/kcse.292 · Cited by 44
A survey of AI ethics in business literature: Maps and trends between 2000 and 2021
Daza, M. T., & Ilozumba, U. J. (2022) · Frontiers in Psychology · doi:10.3389/fpsyg.2022.1042661 · Cited by 40
Self-citationCroes, E. A. J., Antheunis, M. L., Goudbeek, M. B., & Wildman, N. (2022) · International Journal of Human-Computer Interaction · doi:10.1080/10447318.2022.2075574 · Cited by 33
E2S2: Encoding-Enhanced Sequence-to-Sequence Pretraining for Language Understanding and Generation
Zhong, Q., Ding, L., Liu, J., Du, B., & Tao, D. (2023) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2023.3341917 · Cited by 32
A neo-aristotelian perspective on the need for artificial moral agents (AMAs)
Sisón, A. J. G., & Redín, D. M. (2021) · AI & Society · doi:10.1007/s00146-021-01283-0 · Cited by 30
Ali, M. J., & Djalilian, A. R. (2023) · The Ocular Surface · doi:10.1016/j.jtos.2023.04.001 · Cited by 24
Algorithmic Writing Assistance on Jobseekers’ Resumes Increases Hires
Wiles, E., Munyikwa, Z., & Horton, J. J. (2023) · National Bureau of Economic Research · doi:10.3386/w30886 · Cited by 22
Human-Centered Artificial Intelligence: Reliable, Safe & TrustworthyPreprint
Shneiderman, B. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2002.04087 · Cited by 17
DAISY: An Implementation of Five Core Principles for Transparent and Accountable Conversational AI
Wahde, M., & Virgolin, M. (2022) · International Journal of Human-Computer Interaction · doi:10.1080/10447318.2022.2081762 · Cited by 17
What to Discuss?—A Blueprint Topic Model for Health Coaching Dialogues With Conversational Agents
Beinema, T. C., den Akker, H. O., Hermens, H. J., & van Velsen, L. (2022) · International Journal of Human-Computer Interaction · doi:10.1080/10447318.2022.2041884 · Cited by 14
Algorithmic Writing Assistance on Jobseekers' Resumes Increases HiresPreprint
van Inwegen, E., Munyikwa, Z., & Horton, J. J. (2023) · RePEc: Research Papers in Economics · doi:10.48550/arxiv.2301.08083 · Cited by 3
Research works printed but not linked: 11
- Cavoukian, A. (2009). Privacy by design, take the challenge. Information and Privacy Commissioner of Ontario, Canadian Electronic Library. ASIN: B007Z8DS6S.
- Friedland, A. (2023). Meta’s powerful language models leak online, the U.S. Commerce Department opens up applications for CHIPS funds, and a PRC reorganization will impact R&D and data. Center for Security and Emerging Technology.
- Garrido-Merchán, E. C., Arroyo-Barrigüete, J. L., & Gozalo-Brihuela, R. (2023). Simulating HP lovecraft horror literature with the ChatGPT large language model. arXiv Preprint, arXiv:2305.03429. · arXiv
- Hinton G. (2022). The forward-forward algorithm: Some preliminary investigations. arXiv preprint arXiv:2212.13345. · arXiv
- Liu Y. Ott M. Goyal N. Du J. Joshi M. Chen D. Levy O. Lewis M. Zettlemoyer L. & Stoyanov V. (2019). Roberta: A robustly optimized Bert pretraining approach. arXiv Preprint arXiv:1907.11692. · arXiv
- Martín L. A. & Garrido-Merchán E. C. (2021). Many objective Bayesian optimization. arXiv preprint arXiv:2107.04126. · arXiv
- Merchán E. C. G. & Lumbreras S. (2022). On the independence between phenomenal consciousness and computational intelligence. arXiv preprint arXiv:2208.02187. · arXiv
- Mollick, E., & Mollick, L. (2023). Why all our classes suddenly became AI classes. Harvard Business Publishing Education.
- 10.1093/os/9780192845290.001.0001 · DOI
- Van Der Linden, S. (2023). Foolproof: A psychological vaccine against fake news. University of Cambridge. ISBN 9780008466718.
- Zhong Q. Ding L. Liu J. Du B. & Tao D. (2023). Can ChatGPT understand too? A comparative study on ChatGPT and fine-tuned Bert. arXiv preprint arXiv:2302.10198. · arXiv
Cited by
Sorted by citations
Ferrara, E. (2024) · Journal of Computational Social Science · doi:10.1007/s42001-024-00250-1 · Cited by 296
Yu, H. (2024) · Heliyon · doi:10.1016/j.heliyon.2024.e24289 · Cited by 225
RetractedFactuality challenges in the era of large language models and opportunities for fact-checking
Augenstein, I., Baldwin, T., Cha, M., Chakraborty, T., Ciampaglia, G. L., Corney, D., DiResta, R., Ferrara, E., Hale, S., Halevy, A., et al. (2024) · Nature Machine Intelligence · doi:10.1038/s42256-024-00881-z · Cited by 187
Janson, A. (2023) · Computers in Human Behavior · doi:10.1016/j.chb.2023.107954 · Cited by 187
Choudhury, A., & Chaudhry, Z. (2024) · Journal of Medical Internet Research · doi:10.2196/56764 · Cited by 159
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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