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Título: Towards Risk-Free Trustworthy Artificial Intelligence: Significance and Requirements
Autoría: Alzubaidi, L.
Al-Sabaawi, A.
Bai, J.
Dukhan, A.
Alkenani, A.H.
Al-Asadi, A.
Alwzwazy, H.A.
Manoufali, M.
Fadhel, M.A.
Albahri, A.S.
Moreira, C.
Ouyang, C.
Zhang, J.
Santamaria, J.
Salhi, A.
Hollman, F.
Gupta, A.
Duan, Y.
Rabczuk, T.
Abbosh, A.
Gu, Y.
Resumen: Given the tremendous potential and infuence of artifcial intelligence (AI) and algorithmic decision-making (DM), these systems have found wide-ranging applications across diverse felds, including education, business, healthcare industries, government, and justice sectors. While AI and DM ofer signifcant benefts, they also carry the risk of unfavourable outcomes for users and society. As a result, ensuring the safety, reliability, and trustworthiness of these systems becomes crucial. Tis article aims to provide a comprehensive review of the synergy between AI and DM, focussing on the importance of trustworthiness. Te review addresses the following four key questions, guiding readers towards a deeper understanding of this topic: (i) why do we need trustworthy AI? (ii) what are the requirements for trustworthy AI? In line with this second question, the key requirements that establish the trustworthiness of these systems have been explained, including explainability, accountability, robustness, fairness, acceptance of AI, privacy, accuracy, reproducibility, and human agency, and oversight. (iii) how can we have trustworthy data? and (iv) what are the priorities in terms of trustworthy requirements for challenging applications? Regarding this last question, six diferent applications have been discussed, including trustworthy AI in education, environmental science, 5G-based IoTnetworks, robotics for architecture, engineering and construction, fnancial technology, and healthcare. Te review emphasises the need to address trustworthiness in AI systems before their deployment in order to achieve the AI goal for good. An example is provided that demonstrates how trustworthy AI can be employed to eliminate bias in human resources management systems. Te insights and recommendations presented in this paper will serve as a valuable guide for AI researchers seeking to achieve trustworthiness in their applications.
Palabras clave: Deep Learning
Medical Imaging
Explainability
Trustworthy
Fecha: 26-oct-2023
Editorial: Wiley, Hindawi
Citación: Alzubaidi L., Al-Sabaawi A., Bai J., Dukhan A., Alkenani A.H., Al-Asadi A., Alwzwazy H.A., Manoufali M., Fadhel M.A., Albahri A.S., Moreira C., Ouyang C., Zhang J., Santamaría J., Salhi A., Hollman F., Gupta A., Duan Y., Rabczuk T., Abbosh A., Gu Y. Towards Risk-Free Trustworthy Artificial Intelligence: Significance and Requirements International Journal of Intelligent Systems, vol. 2023, Article ID 4459198, 41 pages, 2023. https://doi.org/10.1155/2023/4459198
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