A Trustworthy Human–Machine framework for collective decision making in Food–Energy–Water management: The role of trust sensitivity

Lecture Notes in Networks and Systems

Rivera

et al. 2021

Self Cite

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

Section: Simulated Measurement Resultsmentioning

confidence: 99%

Section: Simulated Measurement Resultsmentioning

confidence: 99%

See 1 more Smart Citation

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

Lecture Notes in Networks and Systems

Rivera

et al. 2021

Self Cite

“…In [24], a measurement theory-based trust management framework was proposed for online social communities. This framework has since been proven to facilitate decisionmaking in multiple areas such as online social networks [25], the food-energy-water nexus [44], crime detection [29], and cancer diagnosis [53]. It is a very flexible yet robust framework that can be adapted to different scenarios to capture trust.…”

Section: Trust Management Frameworkmentioning

confidence: 99%

“…Furthermore, Kaur et al [37] proposed the use of a geo-location and trust-based framework to filter out attackers in 5G social networks. These applications serve as stepping stones toward trustworthy artificial intelligence (AI) and decision making, which have been consistently promoted by researchers [38][39][40][41][42][43][44], governments, institutions, and organizations such as the European Union [45] and the International Organization for Standardization [46].…”

Section: Introductionmentioning

confidence: 99%

Trustability for Resilient Internet of Things Services on 5G Multiple Access Edge Cloud Computing

Durrësi

et al. 2022

Sensors

Self Cite

Billions of Internet of Things (IoT) devices and sensors are expected to be supported by fifth-generation (5G) wireless cellular networks. This highly connected structure is predicted to attract different and unseen types of attacks on devices, sensors, and networks that require advanced mitigation strategies and the active monitoring of the system components. Therefore, a paradigm shift is needed, from traditional prevention and detection approaches toward resilience. This study proposes a trust-based defense framework to ensure resilient IoT services on 5G multi-access edge computing (MEC) systems. This defense framework is based on the trustability metric, which is an extension of the concept of reliability and measures how much a system can be trusted to keep a given level of performance under a specific successful attack vector. Furthermore, trustability is used as a trade-off with system cost to measure the net utility of the system. Systems using multiple sensors with different levels of redundancy were tested, and the framework was shown to measure the trustability of the entire system. Furthermore, different types of attacks were simulated on an edge cloud with multiple nodes, and the trustability was compared to the capabilities of dynamic node addition for the redundancy and removal of untrusted nodes. Finally, the defense framework measured the net utility of the service, comparing the two types of edge clouds with and without the node deactivation capability. Overall, the proposed defense framework based on trustability ensures a satisfactory level of resilience for IoT on 5G MEC systems, which serves as a trade-off with an accepted cost of redundant resources under various attacks.

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

Lecture Notes in Networks and Systems

Durresi

et al. 2021

Self Cite

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.