Critical Assessment of Ethical Implications of Chat GPT

Tobi A. Kareem

Abstract

In our swiftly evolving landscape of artificial intelligence (AI), the discourse surrounding the ethical considerations of AI systems has taken centre stage. A pertinent exemplar in this domain is ChatGPT, an advanced AI model developed by Open AI. It encapsulates the remarkable potential and intricate challenges associated with endowing machines with the capability to engage in human-like conversations and problem-solving tasks. As AI progressively integrates into our daily lives, it becomes imperative to deliberate upon its societal impacts and equitable deployment meticulously. This initiative delves into the intricacies of ChatGPT, elucidating its operational mechanics while shedding light on the critical ethical quandaries that necessitate resolution. In the face of ever-changing technological paradigms, the ethical concerns entwined with AI continue to transform, urging a continuous reassessment of these challenges. This paper seeks to comprehensively investigate the operational framework of ChatGPT with a specific focus on privacy preservation, bias mitigation, and misinformation propagation. Beyond technical aspects, this exploration aspires to capture diverse perspectives from various stakeholders, ensuring a pluralistic incorporation of viewpoints. The overarching objective of this paper lies in fostering a nuanced understanding of the multifaceted ethical dilemmas intrinsic to AI, particularly as embodied by ChatGPT. This article aims to cultivate a shared comprehension of the ethical predicaments at hand by engaging AI developers, users, and policy-makers. As AI systems like ChatGPT progressively assume more substantial roles in our lives, formulating of judicious regulations becomes pivotal to harness its potential effectively while averting potential pitfalls. Delving into the realm of AI ethics transcends the purview of mere analysis; it embodies a collaborative attempt to harness the positive facets of AI for t

Keywords

Artificial Intelligence Chat GPT Ethical Consideration Machine Language

References

Akgun, S. and Greenhow, C. (2021). Artificial intelligence in education: Addressing ethical
challenges
in
K-12
settings.
AI and Ethics, [online] 2(3).
Available at:
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8455229/ [Accessed 29 Aug. 2023].
Akter, S., Dwivedi, Y.K., Sajib, S., Biswas, K., Bandara, R.J. and Michael, K. (2022).
Algorithmic bias in machine learning-based marketing models. Journal of Business
Research,
[online]
144,
pp.201–216.
Available
at:
https://www.sciencedirect.com/science/article/pii/S0148296322000959
[Accessed
29
Aug. 2023].
Blazquez, S.P. and Hipolito, I. (2023). (Machine) Learning to Be Like Thee? For
Algorithm
Education,
Not
Training.
[online]
arXiv.org.
doi:https://doi.org/10.48550/arXiv.2305.12157.
Brundage, M., Guston, D., Fisher, E., Keeler, L. and Bryson, J. (2019). Responsible
Governance of Artificial Intelligence: An Assessment, Theoretical Framework, and
Exploration.
[online]
Available
at:
https://keep.lib.asu.edu/_flysystem/fedora/c7/220491/Brundage_asu_0010E_19562.p
df [Accessed 29 Aug. 2023].
Choudhury, A. and Shamszare, H. (2023). Investigating the Impact of User Trust on the
Adoption and Use of ChatGPT: Survey Analysis. Journal of Medical Internet Research,
[online]
25(1),
p.e47184.
Available
at:
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10337387/ [Accessed 29 Aug. 2023].
Choung, H., David, P. and Seberger, J.S. (2023). A multilevel framework for AI
governance. [online] arXiv.org. doi:https://doi.org/10.48550/arXiv.2307.03198.
Conroy, M., Malik, A.Y., Hale, C., Weir, C., Brockie, A. and Turner, C. (2021). Using
practical wisdom to facilitate ethical decision-making: a major empirical study of
phronesis in the decision narratives of doctors. BMC Medical Ethics, [online] 22(1).
Available at: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7890840/ [Accessed 29
Aug. 2023].
Danish, M.S.S. (2023). AI in Energy: Overcoming Unforeseen Obstacles. AI, [online] 4(2),
pp.406–425. Available at: https://www.mdpi.com/2673-2688/4/2/22 [Accessed 29 Aug.
2023].
Dave, T., Athaluri, S.A. and Singh, S. (2023). ChatGPT in medicine: an overview of its
applications, advantages, limitations, future prospects, and ethical considerations.
Frontiers
in
Artificial
Intelligence,
[online]
Available
at:
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10192861/ [Accessed 29 Aug. 2023].
Davenport, T. and Kalakota, R. (2019). The potential for artificial intelligence in
healthcare. Future Healthcare Journal, [online] 6(2), pp.94–98. Available at:
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6616181/ [Accessed 29 Aug. 2023].
Deranty, J.-P. and Corbin, T. (2022). Artificial intelligence and work: a critical review of
recent research from the social sciences. AI & SOCIETY, [online] pp.1–17. Available at:
https://link.springer.com/article/10.1007/s00146-022-01496-x [Accessed 29 Aug 2023].
Gorman, D.M. (2016). Can We Trust Positive Findings of Intervention Research? The Role
of Conflict of Interest. Prevention Science, [online] 19(3), pp.295–305. Available at:
https://link.springer.com/article/10.1007/s11121-016-0648-1 [Accessed 29 Aug. 2023].
Haleem, A., Javaid, M., Qadri, M.A., Singh, R.P. and Suman, R. (2022). Artificial
Intelligence (AI) Applications for marketing: a literature-based Study. International
Journal
of
Intelligent
Networks,
[online]
3(3),
pp.119–132.
Available
at:
https://www.sciencedirect.com/science/article/pii/S2666603022000136
[Accessed
29
Aug. 2023].
Hassani, H. and Silva, E.S. (2023). The Role of ChatGPT in Data Science: How AI-Assisted
Conversational Interfaces Are Revolutionizing the Field. Big Data and Cognitive
Computing, [online] 7(2), p.62. Available at: https://www.mdpi.com/2504-2289/7/2/62
[Accessed 29 Aug. 2023].
Hopson, J.B., Neji, R., Dunn, J.T., McGinnity, C.J., Flaus, A., Reader, A.J. and Hammers,
A. (2023). Pre-training via Transfer Learning and Pretext Learning a Convolutional
Neural Network for Automated Assessments of Clinical PET Image Quality. IEEE
transactions on radiation and plasma medical sciences, [online] 7(4), pp.372–381.
Available at: https://pubmed.ncbi.nlm.nih.gov/37051163/ [Accessed 29 Aug. 2023].
IBM (2023a). What is Deep Learning? | IBM. [online] www.ibm.com. Available at:
https://www.ibm.com/topics/deep-learning [Accessed 29 Aug. 2023].
Khurana, D., Koli, A., Khatter, K. and Singh, S. (2022). Natural language processing: state
of the art, current trends and challenges. Multimedia Tools and Applications, [online] 82.
Available at: https://link.springer.com/article/10.1007/s11042-022-13428-4 [Accessed 29
Aug. 2023].
Kooli, C. (2023). Chatbots in Education and Research: A Critical Examination of Ethical
Implications and Solutions. Sustainability, [online] 15(7), p.5614. Available at:
https://www.mdpi.com/2071-1050/15/7/5614 [Accessed 29 Aug. 2023].
Korstjens, I. and Moser, A. (2018). Series: Practical Guidance to Qualitative research. Part
4: Trustworthiness and Publishing. European Journal of General Practice, [online] 24(1),
pp.120–124.
Available
at:
https://www.tandfonline.com/doi/abs/10.1080/13814788.2017.1375092
[Accessed
29
Aug. 2023].
Marcelino, P. (2018). Transfer learning from pre-trained models. [online] Medium.
Available at: https://towardsdatascience.com/transfer-learning-from-pre-trainedmodels-
f2393f124751 [Accessed 29 Aug. 2023].
Matsuzaka, Y. and Yashiro, R. (2023). AI-Based Computer Vision Techniques and Expert
Systems. AI, [online] 4(1), pp.289–302. Available at: https://www.mdpi.com/2673-
2688/4/1/13 [Accessed 29 Aug. 2023].
Mittelstadt, B.D. and Floridi, L. (2016). The Ethics of Big Data: Current and Foreseeable
Issues in Biomedical Contexts. Law, Governance and Technology Series, [online] 29,
pp.445–480. Available at: https://link.springer.com/chapter/10.1007/978-3-319-33525-
4_19 [Accessed 29 Aug. 2023]
Shneiderman, B. (2020). Bridging the Gap Between Ethics and Practice. ACM
Transactions on Interactive Intelligent Systems, [online] 10(4), pp.1–31. Available at:
https://dl.acm.org/doi/abs/10.1145/3419764 [Accessed 29 Aug. 2023].
Smith, J.K. (2021). Robotic Persons: Our Future with Social Robots. [online] Google
Books. WestBow Press. [Accessed 29 Aug. 2023].
Sun, H. (2023). Regulating Algorithmic Disinformation. The Columbia Journal of Law &
the
Arts,
[online]
46(4).
Available
at:
https://journals.library.columbia.edu/index.php/lawandarts/article/download/11237/5582
[Accessed 29 Aug. 2023].
Taecharungroj, V. (2023). ‘What Can ChatGPT Do?’ Analyzing Early Reactions to the
Innovative AI Chatbot on Twitter. Big Data and Cognitive Computing, [online] 7(1), p.35.
Available at: https://www.mdpi.com/2504-2289/7/1/35 [Accessed 29 Aug. 2023].
Tai, M.C.-T. (2020). The impact of artificial intelligence on human society and bioethics.
Tzu
Chi
Medical
Journal,
[online]
32(4),
pp.339–343.
Available
at:
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7605294/ [Accessed 29 Aug. 2023].
Thomason, R. (2018). Logic and Artificial Intelligence. Winter 2018 ed. [online] Stanford
Encyclopedia of Philosophy. Available at: https://plato.stanford.edu/entries/logic-ai/
[Accessed 29 Aug. 2023].
Trautman, L.J., Blyden, L., Carr, N., El-Jourbagy, J., Foster, I.I., Green, C., Haugh, T.,
Klaw, B.W., McGee, R.W., Mejia, S., Meyers, K., Sader, E., Schein, D.D. and Sheehan,
C. (2023). Why Study Ethics? [online] Social Science Research Network.
doi:https://doi.org/10.2139/ssrn.4497895.
Trist, E.L. and Bamforth, K.W. (1951). Some Social and Psychological Consequences of the
Longwall Method of Coal-Getting. Human Relations, [online] 4(1), pp.3–38. Available
at:https://journals.sagepub.com/doi/abs/10.1177/001872675100400101
[Accessed
29
Aug. 2023]

Critical Assessment of Ethical Implications of Chat GPT

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