Towards Risk-Free Trustworthy Artificial Intelligence: Significance and Requirements

被引:28
作者
Alzubaidi, Laith [1 ,2 ,3 ]
Al-Sabaawi, Aiman [1 ]
Bai, Jinshuai [1 ]
Dukhan, Ammar [1 ]
Alkenani, Ahmed H. [1 ]
Al-Asadi, Ahmed [4 ,5 ]
Alwzwazy, Haider A. [4 ]
Manoufali, Mohamed [6 ,7 ]
Fadhel, Mohammed A. [2 ]
Albahri, A. S. [8 ,9 ]
Moreira, Catarina [1 ]
Ouyang, Chun [1 ]
Zhang, Jinglan [1 ]
Santamaria, Jose [10 ]
Salhi, Asma [2 ,3 ]
Hollman, Freek [3 ]
Gupta, Ashish [3 ,11 ,12 ]
Duan, Ye [13 ]
Rabczuk, Timon [14 ]
Abbosh, Amin [6 ]
Gu, Yuantong [1 ,3 ]
机构
[1] Queensland Univ Technol, Gardens Point Campus, Brisbane, Qld 4000, Australia
[2] Akunah Co Med Technol, Brisbane, Qld 4120, Australia
[3] Queensland Unit Adv Shoulder Res QUASR, Brisbane, Qld 4000, Australia
[4] Univ Missouri, Elect Engn & Comp Sci Dept, Columbia, MO 65211 USA
[5] Univ Technol Baghdad, Commun Engn Dept, Baghdad 10001, Iraq
[6] Univ Queensland, Sch Informat Technol & Elect Engn, Brisbane, Qld 4067, Australia
[7] CSIRIO, Space & Astron, Kensington, WA 6151, Australia
[8] Univ Pendidikan Sultan Idris UPSI, Fac Comp & Meta Technol FKMT, Tanjung Malim 35900, Malaysia
[9] Imam Jaafar Al Sadiq Univ, Coll Informat Technol, Dept Comp Technol Engn, Baghdad 00964, Iraq
[10] Univ Jaen, Dept Comp Sci, Jaen 23071, Spain
[11] Greenslopes Private Hosp, Brisbane, Qld 4120, Australia
[12] Queensland Univ Technol, Brisbane, Qld 4120, Australia
[13] Clemson Univ, Sch Comp, Clemson, SC 29631 USA
[14] Bauhaus Univ Weimar, Inst Struct Mech, D-99423 Weimar, Germany
基金
澳大利亚研究理事会;
关键词
INFORMATION-TECHNOLOGY; AUTHENTICATION SCHEME; LEARNING-MODELS; FEATURE FUSION; BLACK-BOX; AI; HEALTH; CLASSIFICATION; SECURITY; NETWORK;
D O I
10.1155/2023/4459198
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Given the tremendous potential and influence of artificial intelligence (AI) and algorithmic decision-making (DM), these systems have found wide-ranging applications across diverse fields, including education, business, healthcare industries, government, and justice sectors. While AI and DM offer significant benefits, 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. This article aims to provide a comprehensive review of the synergy between AI and DM, focussing on the importance of trustworthiness. The 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 different applications have been discussed, including trustworthy AI in education, environmental science, 5G-based IoT networks, robotics for architecture, engineering and construction, financial technology, and healthcare. The 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. The insights and recommendations presented in this paper will serve as a valuable guide for AI researchers seeking to achieve trustworthiness in their applications.
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页数:41
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