Decision Support System Using Trust Planning Among Food-Energy-Water Actors

Uslu, Süleyman; Kaur, Davinder; Rivera, Samuel J.; Durresi, Arjan; Babbar-Sebens, Meghna

doi:10.1007/978-3-030-15032-7_98

Cited by 13 publications

(6 citation statements)

References 23 publications

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“…Numerous studies have explored trust modeling and its application in diverse scenarios. For instance, a trust framework proposed by Reference 11, grounded in measurement theory, finds applicability across various domains, including crime detection, 12 social networks, 13,14 the food-energy sector, [15][16][17][18][19][20] healthcare, 21,22 edge computing, [23][24][25][26][27] quantum computing, 28 and beyond. Recent work 21,[29][30][31] has highlighted trust's utility as an acceptance criterion for artificial intelligence algorithms.…”

Section: Role Of Trustmentioning

confidence: 99%

Artificial intelligence control for trust‐based detection of attackers in 5G social networks

Kaur,

Uslu,

Durresi

et al. 2023

Computational Intelligence

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This study introduces a comprehensive framework designed for detecting and mitigating fake and potentially threatening user communities within 5G social networks. Leveraging geo‐location data, community trust dynamics, and AI‐driven community detection algorithms, this framework aims to pinpoint users posing potential harm. Including an artificial control model facilitates the selection of suitable community detection algorithms, coupled with a trust‐based strategy to effectively identify and filter potential attackers. A distinctive feature of this framework lies in its ability to consider attributes that prove challenging for malicious users to emulate, such as the established trust within the community, geographical location, and adaptability to diverse attack scenarios. To validate its efficacy, we illustrate the framework using synthetic social network data, demonstrating its ability to distinguish potential malicious users from trustworthy ones.

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Section: Role Of Trustmentioning

confidence: 99%

Artificial intelligence control for trust‐based detection of attackers in 5G social networks

Kaur,

Uslu,

Durresi

et al. 2023

Computational Intelligence

Self Cite

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“…Our trust framework is validated and applied in various applications such as stock market prediction using twitter [27], fake users detection [19], crime prediction [21], and decision making systems in food-energy-water sectors [30,31,32,34,35,36] .…”

Section: Trust Mechanismmentioning

confidence: 99%

Trustworthy Explainability Acceptance: A New Metric to Measure the Trustworthiness of Interpretable AI Medical Diagnostic Systems

Kaur

Uslu

Durresi

et al. 2021

Lecture Notes in Networks and Systems

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We propose, Trustworthy Explainability Acceptance metric to evaluate explainable AI systems using expert-in-the-loop. Our metric calculates acceptance by quantifying the distance between the explanations generated by the AI system and the reasoning provided by the experts based on their expertise and experience. Our metric also evaluates the trust of the experts to include different groups of experts using our trust mechanism. Our metric can be easily adapted to any Interpretable AI system and be used in the standardization process of trustworthy AI systems. We illustrate the proposed metric using the high-stake medical AI application of Predicting Ductal Carcinoma in Situ (DCIS) Recurrence. Our metric successfully captures the explainability of AI systems in DCIS recurrence by experts.

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“…We introduced a trust-based decision support system for natural resource sharing problems in Food-Energy-Water sectors [48]. Also, we presented the enhanced versions of our system utilizing discrete and precomputed solutions [43], game-theoretical approach [44], and scenarios with different evaluation criteria [45]. Furthermore, we presented the role of trust sensitivity of actors in environmental trust-based decision-making scenarios [47].…”

Section: Related Workmentioning

confidence: 99%

Trustworthy Acceptance: A New Metric for Trustworthy Artificial Intelligence Used in Decision Making in Food–Energy–Water Sectors

Uslu

Kaur

Rivera

et al. 2021

Lecture Notes in Networks and Systems

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We propose, for the first time, a trustworthy acceptance metric and its measurement methodology to evaluate the trustworthiness of AI-based systems used in decision making in Food Energy Water (FEW) management. The proposed metric is a significant step forward in the standardization process of AI systems. It is essential to standardize the AI systems' trustworthiness, but until now, the standardization efforts remain at the level of high-level principles. The measurement methodology of the proposed includes human experts in the loop, and it is based on our trust management system. Our metric captures and quantifies the system's transparent

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Decision Support System Using Trust Planning Among Food-Energy-Water Actors

Cited by 13 publications

References 23 publications

Artificial intelligence control for trust‐based detection of attackers in 5G social networks

Artificial intelligence control for trust‐based detection of attackers in 5G social networks

Trustworthy Explainability Acceptance: A New Metric to Measure the Trustworthiness of Interpretable AI Medical Diagnostic Systems

Trustworthy Acceptance: A New Metric for Trustworthy Artificial Intelligence Used in Decision Making in Food–Energy–Water Sectors

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