Never trust, always verify : a roadmap for Trustworthy AI?

Tidjon, Lionel Nganyewou; Khomh, Foutse

doi:10.48550/arxiv.2206.11981

Cited by 8 publications

(9 citation statements)

References 33 publications

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“…Some suggestions are given to improve social results, such as enhancing social perceptions and performance indicators. Furthermore, AI has shown reproducibility, selecting, and reporting biases [66]. AI systems can misbehave in cases of unreliable data, making them unsafe and untrustworthy [59].…”

Section: The Integrated Ai Frameworkmentioning

confidence: 99%

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An Artificial Intelligence (AI) Framework to Predict Operational Excellence: UAE Case Study

Hassan,

Abu Talib,

Dweiri

et al. 2024

Applied Sciences

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Implementing the European Foundation for Quality Management (EFQM) business excellence model in organizations is time- and cost-consuming. The integration of artificial intelligence (AI) into the EFQM business excellence model is a promising approach to improve the efficiency and effectiveness of excellence in organizations. This research paper’s integrated framework follows the ISO/IEC 23053 standard in addressing some of the concerns related to time and cost associated with the EFQM model, achieving higher EFQM scores, and hence operational excellence. A case study involving a UAE government organization serves as a sample to train the AI framework. Historical EFQM results from different years are used as training data. The AI framework utilizes the unsupervised machine learning technique known as k-means clustering. This technique follows the ISO/IEC 23053 standard to predict EFQM output total scores based on criteria and sub-criteria inputs. This research paper’s main output is a novel AI framework that can predict EFQM scores for organizations at an early stage. If the predicted EFQM score is not high enough, then the AI framework provides feedback to decision makers regarding the criteria that need reconsideration. Continuous use of this integrated framework helps organizations attain operational excellence. This framework is considered valuable for decision makers as it provides early predictions of EFQM total scores and identifies areas that require improvement before officially applying for the EFQM excellence award, hence saving time and cost. This approach can be considered as an innovative contribution and enhancement to knowledge body and organizational practices.

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Section: The Integrated Ai Frameworkmentioning

confidence: 99%

“…AI systems can misbehave in cases of unreliable data, making them unsafe and untrustworthy [59]. ISO 24028 is also used to ensure AI trustworthiness [66].…”

Section: The Integrated Ai Frameworkmentioning

confidence: 99%

An Artificial Intelligence (AI) Framework to Predict Operational Excellence: UAE Case Study

Hassan,

Abu Talib,

Dweiri

et al. 2024

Applied Sciences

View full text Add to dashboard Cite

show abstract

“…Some suggestions are given to improve social results such as enhancing social perceptions and performance indicators. Furthermore, AI showed reproducibility, selecting, and reporting biases [42]. AI systems can misbehave towards unreliable data making them unsafe and untrustworthy.…”

Section: The Integrated Ai Frameworkmentioning

confidence: 99%

“…AI systems can misbehave towards unreliable data making them unsafe and untrustworthy. ISO 24028 is also used to ensure AI trustworthiness [42].…”

Section: The Integrated Ai Frameworkmentioning

confidence: 99%

An Artificial Intelligence (AI) Framework To Predict Operational Excellence: UAE Case Study

Hassan,

Abu Talib,

Dweiri

et al. 2024

Preprint

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The integration of artificial intelligence (AI) into the European Foundation for Quality Management (EFQM) business excellence model is a promising approach to improve the efficiency and effectiveness of excellence in organizations. This research paper’s integrated framework follows the ISO/IEC 23053 standard in addressing some of the concerns related to time and cost associated with the EFQM model, achieving higher EFQM scores, hence operational excellence. A case study involving a UAE government organization serves as a sample to train the AI framework. Historical EFQM results from different years are used as training data. The AI framework utilizes the unsupervised machine learning technique known as k-means clustering (with k=2). This technique follows the ISO/IEC 23053 standard to predict EFQM output total scores based on criteria and sub-criteria inputs. The research paper's main output is a novel AI framework that can predict EFQM scores for organizations at an early stage. If the predicted EFQM score is not high enough, then the AI framework provides feedback to decision makers regarding the criteria that need reconsideration. Continuous use of this integrated framework helps organizations attain operational excellence. This framework is considered valuable for decision makers as it provides early predictions of EFQM total scores and identifies areas that require improvement before officially applying for the EFQM excellence award. This approach can be considered as an innovative contribution and enhancement to knowledge body and organizational practices.

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“…For zero trust, AI, as a human analogue, is becoming one-sided, in terms of assessing security using technical metrics alone. Tidjon et al [20] attempted to understand the factors influencing the trustworthiness of an AI system. By compiling and summarizing the literature, transparency was found to be the most adopted principle.…”

Section: What Is Zero Trustmentioning

confidence: 99%

Theory and Application of Zero Trust Security: A Brief Survey

Kang,

Liu,

Wang

et al. 2023

Entropy

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As cross-border access becomes more frequent, traditional perimeter-based network security models can no longer cope with evolving security requirements. Zero trust is a novel paradigm for cybersecurity based on the core concept of “never trust, always verify”. It attempts to protect against security risks related to internal threats by eliminating the demarcations between the internal and external network of traditional network perimeters. Nevertheless, research on the theory and application of zero trust is still in its infancy, and more extensive research is necessary to facilitate a deeper understanding of the paradigm in academia and the industry. In this paper, trust in cybersecurity is discussed, following which the origin, concepts, and principles related to zero trust are elaborated on. The characteristics, strengths, and weaknesses of the existing research are analysed in the context of zero trust achievements and their technical applications in Cloud and IoT environments. Finally, to support the development and application of zero trust in the future, the concept and its current challenges are analysed.

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Never trust, always verify : a roadmap for Trustworthy AI?

Cited by 8 publications

References 33 publications

An Artificial Intelligence (AI) Framework to Predict Operational Excellence: UAE Case Study

An Artificial Intelligence (AI) Framework to Predict Operational Excellence: UAE Case Study

An Artificial Intelligence (AI) Framework To Predict Operational Excellence: UAE Case Study

Theory and Application of Zero Trust Security: A Brief Survey

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