The Considerations of Trustworthy AI Components in Generative AI; A Letter to Editor
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Dear Editor: In navigating the ever-expanding realms of Artificial Intelligence (AI) across diverse domains, particularly within the purviews of Generative AI, it becomes imperative to delve into the intricate considerations of trustworthy AI components [1]. This letter aims to underscore the salient aspects of this discussion with a specific focus on the thematic areas championed by the journal.
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[1] A. S. Albahri et al., “A systematic review of trustworthy and explainable artificial intelligence in healthcare: Assessment of quality, bias risk, and data fusion,” Inf. Fusion, vol. 96, pp. 156–191, Mar. 2023, doi: 10.1016/j.inffus.2023.03.008.
[2] L. Alzubaidi et al., “Towards Risk-Free Trustworthy Artificial Intelligence: Significance and Requirements,” Int. J. Intell. Syst., vol. 2023, p. 4459198, 2023, doi: 10.1155/2023/4459198.
[3] K. B. Ooi et al., “The Potential of Generative Artificial Intelligence Across Disciplines: Perspectives and Future Directions,” J. Comput. Inf. Syst., pp. 1–32, 2023, doi: 10.1080/08874417.2023.2261010.
[4] P. P. Ray, “ChatGPT: A comprehensive review on background, applications, key challenges, bias, ethics, limitations and future scope,” Internet Things Cyber-Physical Syst., vol. 3, pp. 121–154, 2023, doi: 10.1016/j.iotcps.2023.04.003.
[5] WHO, “Ethics and Governance of Artificial Intelligence for Health: WHO guidance,” World Heal. Organ., pp. 1–148, 2021.
[6] C. Adams, P. Pente, G. Lemermeyer, and G. Rockwell, “Ethical principles for artificial intelligence in K-12 education,” Comput. Educ. Artif. Intell., vol. 4, no. 4, pp. 4221–4241, 2023, doi: 10.1016/j.caeai.2023.100131.
[7] A. S. Albahri et al., “A Systematic Review of Using Deep Learning Technology in the Steady-State Visually Evoked Potential-Based Brain-Computer Interface Applications: Current Trends and Future Trust Methodology,” Int. J. Telemed. Appl., vol. 2023, 2023, doi: 10.1155/2023/7741735.
[8] M. Baydaş and D. Pamučar, “Determining Objective Characteristics of MCDM Methods under Uncertainty: An Exploration Study with Financial Data,” Mathematics, vol. 10, no. 7, p. 1115, 2022, doi: 10.3390/math10071115.
[9] S. S. Joudar, A. S. Albahri, and R. A. Hamid, “Intelligent triage method for early diagnosis autism spectrum disorder (ASD) based on integrated fuzzy multi-criteria decision-making methods,” Informatics Med. Unlocked, vol. 36, p. 101131, 2023, doi: 10.1016/j.imu.2022.101131.
[10] N. S. Baqer et al., “Indoor air quality pollutants predicting approach using unified labelling process-based multi-criteria decision making and machine learning techniques,” Telecommun. Syst., vol. 81, no. 4, pp. 591–613, Dec. 2022, doi: 10.1007/s11235-022-00959-2.