Prognosis of exploration on Chat GPT with artificial intelligence ethics
DOI:
https://doi.org/10.14295/bjs.v2i9.372Keywords:
natural language processing, artificial intelligence, ethics, openAI's chatGPT3, large language modelsAbstract
Natural language processing innovations in the past few decades have made it feasible to synthesis and comprehend coherent text in a variety of ways, turning theoretical techniques into practical implementations. Both report summarizing software and sectors like content writers have been significantly impacted by the extensive Language-model. A huge language model, however, could show evidence of social prejudice, giving moral as well as environmental hazards from negligence, according to observations. Therefore, it is necessary to develop comprehensive guidelines for responsible LLM (Large Language Models). Despite the fact that numerous empirical investigations show that sophisticated large language models has very few ethical difficulties, there isn't a thorough investigation and consumers study of the legality of present large language model use. We use a qualitative study method on OpenAI's ChatGPT3 to solution-focus the real-world ethical risks in current large language models in order to further guide ongoing efforts on responsibly constructing ethical large language models. We carefully review ChatGPT3 from the four perspectives of bias and robustness. According to our stated opinions, we objectively benchmark ChatGPT3 on a number of sample datasets. In this work, it was found that a substantial fraction of principled problems are not solved by the current benchmarks; therefore new case examples were provided to support this. Additionally discussed were the importance of the findings regarding ChatGPT3's AI ethics, potential problems in the future, and helpful design considerations for big language models. This study may provide some guidance for future investigations into and mitigation of the ethical risks offered by technology in large Language Models applications.
References
Alshahrani, H. J., Tarmissi, K., Alshahrani, H., Ahmed Elfaki, M., Yafoz, A., Alsini, R., & Ahmed Hamza, M. (2022). Computational Linguistics with Deep-Learning-Based Intent Detection for Natural Language Understanding. Applied Sciences, 12(17), 8633. https://doi.org/10.3390/app12178633 DOI: https://doi.org/10.3390/app12178633
Burstein, J., Doran, C., & Solorio, T. (2019). Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers). In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers). https://aclanthology.org/N19-1
Dahmen, J., Kayaalp, M., Ollivier, M., Pareek, A., Hirschmann, M. T., Karlsson, J., & Winkler, P. W. (2023). Artificial intelligence bot ChatGPT in medical research: the potential game changer as a double-edged sword. Knee Surgery, Sports Traumatology, Arthroscopy, 1187-1189. https://doi.org/10.1007/s00167-023-07355-6 DOI: https://doi.org/10.1007/s00167-023-07355-6
Dror, R., Baumer, G., Shlomov, S., & Reichart, R. (2018). The hitchhiker’s guide to testing statistical significance in natural language processing. In: Proceedings of the 56th annual meeting of the association for computational linguistics, 1, Long papers, 1383-1392. http://dx.doi.org/10.18653/v1/P18-1128 DOI: https://doi.org/10.18653/v1/P18-1128
Eliot, D., & Wood, D. M. (2021). Minding the FLoCs: Google’s marketing moves, AI, privacy and the data commons. GIGI Online. https://www.cigionline.org/articles/minding-flocs-googles-marketing-moves-ai-privacy-and-data-commons/
Feder, A., Keith, K. A., Manzoor, E., Pryzant, R., Sridhar, D., Wood-Doughty, Z., & Yang, D. (2022). Causal inference in natural language processing: Estimation, prediction, interpretation and beyond. Transactions of the Association for Computational Linguistics, 10, 1138-1158. https://direct.mit.edu/tacl/article/doi/10.1162/tacl_a_00511/113490/Causal-Inference-in-Natural-Language-Processing DOI: https://doi.org/10.1162/tacl_a_00511
Gao, C. A., Howard, F. M., Markov, N. S., Dyer, E. C., Ramesh, S., Luo, Y., & Pearson, A. T. (2022). Comparing scientific abstracts generated by ChatGPT to original abstracts using an artificial intelligence output detector, plagiarism detector, and blinded human reviewers. bioRxiv. https://doi.org/10.1101/2022.12.23.521610 DOI: https://doi.org/10.1101/2022.12.23.521610
Hirschberg, J., & Manning, C. D. (2015). Advances in natural language processing. Science, 349(6245), 261-266. https://doi.org/10.1126/science.aaa8685 DOI: https://doi.org/10.1126/science.aaa8685
Hovy, D., & Spruit, S. L. (2016). The social impact of natural language processing. In: Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics, 2, Short Papers, 591-598. https://aclanthology.org/P16-2096.pdf DOI: https://doi.org/10.18653/v1/P16-2096
Jiang, J. Y., Sun, X., Wang, W., & Young, S. (2019). Enhancing air quality prediction with social media and natural language processing. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, 2627-2632. http://dx.doi.org/10.18653/v1/P19-1251 DOI: https://doi.org/10.18653/v1/P19-1251
Johnson, T., & Obradovich, N. (2022). Measuring an artificial intelligence agent's trust in humans using machine incentives. ArXiv, preprint arXiv:2212.13371. https://doi.org/10.48550/arXiv.2212.13371
Kurian, N., Cherian, J. M., Sudharson, N. A., Varghese, K. G., & Wadhwa, S. (2023). AI is now everywhere. British Dental Journal, 234(2), 72-72. https://doi.org/10.1038/s41415-023-5461-1 DOI: https://doi.org/10.1038/s41415-023-5461-1
Loconte, R., Orrù, G., Tribastone, M., Pietrini, P., & Sartori, G. (2023). Challenging ChatGPT' Intelligence' with human tools: A neuropsychological investigation on prefrontal functioning of a large language model. intelligence. 33 p. https://dx.doi.org/10.2139/ssrn.4377371 DOI: https://doi.org/10.2139/ssrn.4377371
Otter, D. W., Medina, J. R., & Kalita, J. K. (2020). A survey of the usages of deep learning for natural language processing. IEEE Transactions on Neural Networks and Learning Systems, 32(2), 604-624. https://doi.org/10.1109/TNNLS.2020.2979670 DOI: https://doi.org/10.1109/TNNLS.2020.2979670
Qi, P., Zhang, Y., Zhang, Y., Bolton, J., & Manning, C. D. (2020). Stanza: A Python natural language processing toolkit for many human languages. AArXiv. arXiv:2003.07082. https://doi.org/10.48550/arXiv.2003.07082 DOI: https://doi.org/10.18653/v1/2020.acl-demos.14
Raza, S., & Schwartz, B. (2023). Entity and relation extraction from clinical case reports of COVID-19: a natural language processing approach. BMC Medical Informatics and Decision Making, 23(1), 20. https://doi.org/10.1186/s12911-023-02117-3 DOI: https://doi.org/10.1186/s12911-023-02117-3
Sharma, P., & Dash, B. (2020). Impact of big data analytics and ChatGPT on cybersecurity.
Sun, T. X., Liu, X. Y., Qiu, X. P., & Huang, X. J. (2022). Paradigm shift in natural language processing. Machine Intelligence Research, 19(3), 169-183. https://doi.org/10.1007/s11633-022-1331-6 DOI: https://doi.org/10.1007/s11633-022-1331-6
Zhang, R., Ji, Y., Zhang, Y., & Passonneau, R. J. (2022). Contrastive Data and Learning for Natural Language Processing. In Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies: Tutorial Abstracts, 39-47. DOI: https://doi.org/10.18653/v1/2022.naacl-tutorials.6
Zhuo, T. Y., Huang, Y., Chen, C., & Xing, Z. (2023). Exploring ai ethics of chatgpt: A diagnostic analysis. ArXiv. arXiv:2301.12867.
Ward, S. F., & Tashea, J. (2020). Some law schools offer tech programs to help students find jobs, but does it work?. Aba Journal. https://www.abajournal.com/magazine/article/law-school-technology-programs?icn=most_read
Worth, P. J. (2023). Word embeddings and semantic spaces in natural language processing. International Journal of Intelligence Science, 13(1), 1-21. https://doi.org/10.4236/ijis.2023.131001 DOI: https://doi.org/10.4236/ijis.2023.131001
Moreb, M., Salah, S., & Amro, B. (2023). A novel framework for mobile forensics investigation process. Research Square, 1-20. https://doi.org/10.21203/rs.3.rs-2611927/v1 DOI: https://doi.org/10.21203/rs.3.rs-2611927/v1
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2023 N. Gowri Vidhya, Anna Devi, Nithya Shree, T. Manju
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors who publish with this journal agree to the following terms:
1) Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
2) Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
3) Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work.
