Measuring Perceived Trust in XAI-Assisted Decision-Making by Eliciting a Mental Model

This empirical study proposes a novel methodology to measure users' perceived trust in an Explainable Artificial Intelligence (XAI) model. To do so, users' mental models are elicited using Fuzzy Cognitive Maps (FCMs). First, we exploit an interpretable Machine Learning (ML) model to classify suspected COVID-19 patients into positive or negative cases. Then, Medical Experts' (MEs) conduct a diagnostic decision-making task based on their knowledge and then prediction and interpretations provided by the XAI model. In order to evaluate the impact of interpretations on perceived trust, explanation satisfaction attributes are rated by MEs through a survey. Then, they are considered as FCM's concepts to determine their influences on each other and, ultimately, on the perceived trust. Moreover, to consider MEs' mental subjectivity, fuzzy linguistic variables are used to determine the strength of influences. After reaching the steady state of FCMs, a quantified value is obtained to measure the perceived trust of each ME. The results show that the quantified values can determine whether MEs trust or distrust the XAI model. We analyze this behavior by comparing the quantified values with MEs' performance in completing diagnostic tasks

Measuring Perceived Trust in XAI-Assisted Decision-Making by Eliciting a Mental Model

This empirical study proposes a novel methodology to measure users' perceived trust in an Explainable Artificial Intelligence (XAI) model. To do so, users' mental models are elicited using Fuzzy Cognitive Maps (FCMs). First, we exploit an interpretable Machine Learning (ML) model to classify suspected COVID-19 patients into positive or negative cases. Then, Medical Experts' (MEs) conduct a diagnostic decision-making task based on their knowledge and then prediction and interpretations provided by the XAI model. In order to evaluate the impact of interpretations on perceived trust, explanation satisfaction attributes are rated by MEs through a survey. Then, they are considered as FCM's concepts to determine their influences on each other and, ultimately, on the perceived trust. Moreover, to consider MEs' mental subjectivity, fuzzy linguistic variables are used to determine the strength of influences. After reaching the steady state of FCMs, a quantified value is obtained to measure the perceived trust of each ME. The results show that the quantified values can determine whether MEs trust or distrust the XAI model. We analyze this behavior by comparing the quantified values with MEs' performance in completing diagnostic tasks.Comment: Accepted in IJCAI 2023 Workshop on Explainable Artificial Intelligence (XAI

Implementing bargaining game-based fuzzy cognitive map and mixed-motive games for group decisions in the healthcare supplier selection

Evaluating and selecting proper suppliers in the healthcare centers due to their high impact on the financial situation and citizens’ satisfaction is vital. The abundance of various criteria affecting the Supplier Selection (SS) problem makes it a decision-making problem. To this end, an approach according to the Bargaining Game-based Fuzzy Cognitive Map (BG-FCM) and mixed-motive games has been proposed for simultaneously modeling the SS complexity and suppliers’ competition in the market. First, according to the BG-FCM, the causal relationships between SS criteria have been determined. Then, by implementing Particle Swarm Optimization and the S-shaped transfer function (PSO-STF) and scenario-making, the BG-FCM is executed to extract robust payoffs for suppliers to compete. The competition between suppliers is modeled by mixed-motive games and their robust payoffs to determine their best strategies in the competition. Finally, suppliers compete with each other two by two, and suppliers with the most wins will have higher priority. The proposed approach has been applied in a general hospital to evaluate major suppliers for purchasing necessities. Then, it is compared with two well-known Multi-Criteria Decision Making (MCDM) approaches, showing a better performance in modeling the complexity and competition in the problem. The proposed approach can help the hospital select the most appropriate suppliers according to its preferences and avoid cooperating with inappropriate suppliers, which may cause a low-quality Supply Chain (SC) system or financial calamities.</p

Trustworthy Artificial Intelligence in Medical Applications: A Mini Survey

Nowadays, a large amount of structured and unstructured data is being produced in various fields, creating tremendous opportunities to implement Machine Learning (ML) algorithms for decision-making. Although ML algorithms can outperform human performance in some fields, the black-box inherent characteristics of advanced models can hinder experts from exploiting them in sensitive domains such as medicine. The black-box nature of advanced ML models shadows the transparency of these algorithms, which could hamper their fair and robust performance due to the complexity of the algorithms. Consequently, individuals, organizations, and societies will not be able to achieve the full potential of ML without establishing trust in its development, deployment, and use. The field of eXplainable Artificial Intelligence (XAI) endeavors to solve this problem by providing human-understandable explanations for black-box models as a potential solution to acquire trustworthy AI. However, explainability is one of many requirements to fulfill trustworthy AI, and other prerequisites must also be met. Hence, this survey analyzes the fulfillment of five algorithmic requirements of accuracy, transparency, trust, robustness, and fairness through the lens of the literature in the medical domain. Regarding that medical experts are reluctant to put their judgment aside in favor of a machine, trustworthy AI algorithmic fulfillment could be a way to convince them to use ML. The results show there is still a long way to implement the algorithmic requirements in practice, and scholars need to consider them in future studies

Trustworthy Artificial Intelligence in Medical Applications: A Mini Survey

Nowadays, a large amount of structured and unstructured data is being produced in various fields, creating tremendous opportunities to implement Machine Learning (ML) algorithms for decision-making. Although ML algorithms can outperform human performance in some fields, the black-box inherent characteristics of advanced models can hinder experts from exploiting them in sensitive domains such as medicine. The black-box nature of advanced ML models shadows the transparency of these algorithms, which could hamper their fair and robust performance due to the complexity of the algorithms. Consequently, individuals, organizations, and societies will not be able to achieve the full potential of ML without establishing trust in its development, deployment, and use. The field of eXplainable Artificial Intelligence (XAI) endeavors to solve this problem by providing human-understandable explanations for black-box models as a potential solution to acquire trustworthy AI. However, explainability is one of many requirements to fulfill trustworthy AI, and other prerequisites must also be met. Hence, this survey analyzes the fulfillment of five algorithmic requirements of accuracy, transparency, trust, robustness, and fairness through the lens of the literature in the medical domain. Regarding that medical experts are reluctant to put their judgment aside in favor of a machine, trustworthy AI algorithmic fulfillment could be a way to convince them to use ML. The results show there is still a long way to implement the algorithmic requirements in practice, and scholars need to consider them in future studies

Trustworthy Artificial Intelligence in Medical Applications: A Mini Survey

Nowadays, a large amount of structured and unstructured data is being produced in various fields, creating tremendous opportunities to implement Machine Learning (ML) algorithms for decision-making. Although ML algorithms can outperform human performance in some fields, the black-box inherent characteristics of advanced models can hinder experts from exploiting them in sensitive domains such as medicine. The black-box nature of advanced ML models shadows the transparency of these algorithms, which could hamper their fair and robust performance due to the complexity of the algorithms. Consequently, individuals, organizations, and societies will not be able to achieve the full potential of ML without establishing trust in its development, deployment, and use. The field of eXplainable Artificial Intelligence (XAI) endeavors to solve this problem by providing human-understandable explanations for black-box models as a potential solution to acquire trustworthy AI. However, explainability is one of many requirements to fulfill trustworthy AI, and other prerequisites must also be met. Hence, this survey analyzes the fulfillment of five algorithmic requirements of accuracy, transparency, trust, robustness, and fairness through the lens of the literature in the medical domain. Regarding that medical experts are reluctant to put their judgment aside in favor of a machine, trustworthy AI algorithmic fulfillment could be a way to convince them to use ML. The results show there is still a long way to implement the algorithmic requirements in practice, and scholars need to consider them in future studies

Enhancing risk assessment of manufacturing production process integrating failure modes and sequential fuzzy cognitive map

When a risk occurs in a stage of the production process, it can be due to the risks of the previous stages, or it is effective in causing the risks in the later stages. The current paper proposes an intelligent approach based on cause-and-effect relationships to assess and prioritize a manufacturing unit’s risks. Sequential multi-stage fuzzy cognitive maps (MSFCMs) are used for drawing the map of risks. Then, the learning algorithm is implemented for learning the MSFCM and finalizing the risks score. A case study on an auto-parts manufacturing unit is applied to demonstrate the capabilities of the proposed approach