We propose that intelligently combining models from the domains of Artificial Intelligence or Machine Learning with Physical and Expert models will yield a more "trustworthy" model than any one model from a single domain, given a complex and narrow enough problem. Based on mean-variance portfolio theory and bias-variance trade-off analysis, we prove combining models from various domains produces a model that has lower risk, increasing user trust. We call such combined models - physics enhanced artificial intelligence (PEAI), and suggest use cases for PEAI.
Other ways to improve user trust and adoption in AI include providing tractable performance improvement compared to non-AI systems in controlled data set and experiments. This is normally achieved by building metrics to quantify performance @cite_7 . However, any quantitative comparison will likely tie the performance to the testing data sets with known data bias being an issue for AI systems @cite_4 . Another practical way to restoring user trust for AI services is by providing supplementary documentation such as a supplier's declaration of conformity (SDoC) for the AI services provided @cite_16 .
@cite_16 @cite_4 @cite_7
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Accuracy is an important concern for suppliers of artificial intelligence (AI) services, but considerations beyond accuracy, such as safety (which includes fairness and explainability), security, and provenance, are also critical elements to engender consumers' trust in a service. Many industries use transparent, standardized, but often not legally required documents called supplier's declarations of conformity (SDoCs) to describe the lineage of a product along with the safety and performance testing it has undergone. SDoCs may be considered multi-dimensional fact sheets that capture and quantify various aspects of the product and its development to make it worthy of consumers' trust. Inspired by this practice, we propose FactSheets to help increase trust in AI services. We envision such documents to contain purpose, performance, safety, security, and provenance information to be completed by AI service providers for examination by consumers. We suggest a comprehensive set of declaration items tailored to AI and provide examples for two fictitious AI services in the appendix of the paper. Abstract Given the increasing applications of service computing and cloud computing, a large number of Web services are deployed on the Internet, triggering the research of Web service recommendation. Despite of service QoS, the use of user feedback is becoming the current trend in service recommendation. Likewise in traditional recommender systems, sparsity, cold-start and trustworthiness are major issues challenging service recommendation in adopting similarity-based approaches. Meanwhile, with the prevalence of social networks, nowadays people become active in interacting with various computers and users, resulting in a huge volume of data available, such as service information, user-service ratings, interaction logs, and user relationships. Therefore, how to incorporate the trust relationship in social networks with user feedback for service recommendation motivates this work. In this paper, we propose a social network-based service recommendation method with trust enhancement known as RelevantTrustWalker. First, a matrix factorization method is utilized to assess the degree of trust between users in social network. Next, an extended random walk algorithm is proposed to obtain recommendation results. To evaluate the accuracy of the algorithm, experiments on a real-world dataset are conducted and experimental results indicate that the quality of the recommendation and the speed of the method are improved compared with existing algorithms. We present a shared-control framework predicated on a measure of trust in the operator, that is calculated automatically based on the quality of the interactions between a human and autonomous system. This measure of trust is built upon a control-theoretic foundation that rewards stable operation of the system to give more trusted users additional control authority. The level of control authority is used to modify the human input, and as a result, we observe a minimization of the required effort of the controller. We validate this work within a planar crane environment with a receding horizon controller to assist with the regulation of the system dynamics. The human defines the reference trajectory for the controller. In an experimental study users navigate a suspended payload through a set of maze configurations. We find that adaptation of the trust metric over time provides the benefit of substantially ( @math ) improving the automated system's ability to modulate the user's input, resulting in stable reference trajectories that require less effort to track. In effect, the human and automation spend less time fighting each other during task execution, suggesting that the automated system and user each have a better understanding of the other's ability. We propose a learning automata-based method for optimizing membership functions.The proposed method adjusts the number and the position of membership functions.The proposed method can be used without any change in any fuzzy recommender system.The performance of proposed method is tested on well-known datasets.The results show that the proposed method improves the recommendation accuracy. Incorporating trust and distrust information into collaborative recommender systems alleviates data sparsity and cold start problems. Since trust and distrust are a gradual phenomenon, they can be stated more naturally by fuzzy logic. Finding the most appropriate fuzzy sets which cover the domains of trust and distrust is not an easy task. Existing research on fuzzy modelling of trust and distrust has not considered the optimization of membership functions. In this paper, we address this issue and propose a continuous action-set learning automata (CALA)-based method to adjust membership functions of fuzzy trust and distrust during the lifetime of recommender system in terms of recommendation error. By assigning a CALA to the centre parameter of each triangular membership function, the proposed method optimizes the number and the position of fuzzy sets. To the best of our knowledge, this is the first effort in this direction. The experimental results indicate that using the proposed method in fuzzy recommender systems improves the recommendation accuracy.