Towards Risk‐Free Trustworthy Artificial Intelligence: Significance and Requirements

Laith Alzubaidi · Aiman Al-Sabaawi · Jinshuai Bai · Ammar Dukhan · Ahmed H. Alkenani · Ahmed Al‐Asadi +15 more

91 citations0/0 files storedInternational Journal of Intelligent Systems

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Ethics and Social Impacts of AI Explainable Artificial Intelligence (XAI)Safety Systems Engineering in Autonomy

Abstract

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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References

2012Consumer Acceptance and Use of Information Technology: Extending the Unified Theory of Acceptance and Use of Technology1 2019The global landscape of AI ethics guidelines 2021A Survey on Bias and Fairness in Machine Learning 2012Fairness through awareness 2019Principles alone cannot guarantee ethical AI 2020Human-Centered Artificial Intelligence: Reliable, Safe & Trustworthy 2019A Unified Framework of Five Principles for AI in Society 2019Review of “Race After Technology: Abolitionist Tools for the New Jim Code”

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2025Impact of AI applications on corporate green innovation 2025Requirements for trustworthy AI-enabled automated decision-making in the public sector: A systematic review 2025Is artificial intelligence (AI) research biased and conceptually vague? A systematic review of research on bias and discrimination in the context of using AI in human resource management 2025Advancing AI governance with a unified theoretical framework: a systematic review 2025AI Adoption in the Finance Sector of a Developing Economy

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