In recent decades,social scientists have debated declining levels of trust in American institutions.At the same time,many American institutions are coming under scrutiny for their use of artificial intelligence(AI)sys...In recent decades,social scientists have debated declining levels of trust in American institutions.At the same time,many American institutions are coming under scrutiny for their use of artificial intelligence(AI)systems.This paper analyzes the results of a survey experiment over a nationally representative sample to gauge the effect that the use of AI has on the American public’s trust in their social institutions,including government,private corporations,police precincts,and hospitals.We find that artificial intelligence systems were associated with significant trust penalties when used by American police precincts,companies,and hospitals.These penalties were especially strong for American police precincts and,in most cases,were notably stronger than the trust penalties associated with the use of smartphone apps,implicit bias training,machine learning,and mindfulness training.Americans’trust in institutions tends to be negatively impacted by the use of new tools.While there are significant variations in trust between different pairings of institutions and tools,generally speaking,institutions which use AI suffer the most significant loss of trust.American government agencies are a notable exception here,receiving a small but puzzling boost in trust when associated with the use of AI systems.展开更多
With the rapid development of social network in recent years, a huge number of social information has been produced. As traditional recommender systems often face data sparsity and cold-start problem, the use of socia...With the rapid development of social network in recent years, a huge number of social information has been produced. As traditional recommender systems often face data sparsity and cold-start problem, the use of social information has attracted many researchers' attention to improve the prediction accuracy of recommender systems. Social trust and social relation have been proven useful to improve the performance of recommendation. Based on the classic collaborative filtering technique, we propose a PCCTTF recommender method that takes the rating time of users, social trust among users, and item tags into consideration, then do the item recommending. Experimental results show that the PCCTTF method has better prediction accuracy than classical collaborative filtering technique and the state-of-the-art recommender methods, and can also effectively alleviate data sparsity and cold-start problem. Furthermore, the PCCTTF method has better performance than all the compared methods while counting against shilling attacks.展开更多
Inferring unknown social trust relations attracts increasing attention in recent years. However, social trust, as a social concept, is intrinsically dynamic, and exploiting temporal dynamics provides challenges and op...Inferring unknown social trust relations attracts increasing attention in recent years. However, social trust, as a social concept, is intrinsically dynamic, and exploiting temporal dynamics provides challenges and opportunities for social trust prediction. In this paper, we investigate social trust prediction by exploiting temporal dynamics. In particular, we model the dynamics of user preferences in two principled ways. The first one focuses on temporal weight; the second one targets temporal smoothness. By incorporating these two types of temporal dynamics into traditional matrix factorization based social trust prediction model, two extended social trust prediction models are proposed and the cor- responding algorithms to solve the models are designed too. We conduct experiments on a real-world dataset and the results dem- onstrate the effectiveness of our proposed new models. Further experiments are also conducted to understand the importance of temporal dynamics in social trust prediction.展开更多
With the rapid development of social networks, there is a focus on marketing strategies and business models that are based on social media. In the academic world, scholars believe that online trust is a key factor con...With the rapid development of social networks, there is a focus on marketing strategies and business models that are based on social media. In the academic world, scholars believe that online trust is a key factor contributing to online purchasing behavior. This article explored several factors in social media trust and verified the moderating role of offline familiarity by using relevant research on online trust in conjunction with a structure equation model. The results show that independent variables such as reputation, SNS interaction, information quality, reciprocity, satisfaction and shared values have a positive influence on trust, whereas perceived similarity does not, and information quality is the most important factor. In addition, offline familiarity significantly moderates the relations between information quality, reciprocity, reputation, shared values and social media trust. This information is important to assist companies in developing an effective social network marketing strategy.展开更多
Recent years we have witnessed the rapid growth of social commerce in China, but many users are not willing to trust and use social commerce. So improving consumers’ trust and purchase intention has become a crucial ...Recent years we have witnessed the rapid growth of social commerce in China, but many users are not willing to trust and use social commerce. So improving consumers’ trust and purchase intention has become a crucial factor in the success of social commerce. Business factors, environment factors and social factors including twelve secondary indexes build up a social commerce trust evaluation model. Questionnaires are handed out to collect twelve secondary indexes scores as input of BP neural network and composite score of trust as output. Model simulation shows that both training samples and test samples have low level of average error and standard deviation, which certify that the model has good stability and it is a good method for evaluating social commerce trust.展开更多
基金supported by the National Science Foundation(Nos.IIS-1927227 and CCF-2208664).
文摘In recent decades,social scientists have debated declining levels of trust in American institutions.At the same time,many American institutions are coming under scrutiny for their use of artificial intelligence(AI)systems.This paper analyzes the results of a survey experiment over a nationally representative sample to gauge the effect that the use of AI has on the American public’s trust in their social institutions,including government,private corporations,police precincts,and hospitals.We find that artificial intelligence systems were associated with significant trust penalties when used by American police precincts,companies,and hospitals.These penalties were especially strong for American police precincts and,in most cases,were notably stronger than the trust penalties associated with the use of smartphone apps,implicit bias training,machine learning,and mindfulness training.Americans’trust in institutions tends to be negatively impacted by the use of new tools.While there are significant variations in trust between different pairings of institutions and tools,generally speaking,institutions which use AI suffer the most significant loss of trust.American government agencies are a notable exception here,receiving a small but puzzling boost in trust when associated with the use of AI systems.
基金Supported by the National Natural Science Foundation of China(71662014,61602219,71861013)。
文摘With the rapid development of social network in recent years, a huge number of social information has been produced. As traditional recommender systems often face data sparsity and cold-start problem, the use of social information has attracted many researchers' attention to improve the prediction accuracy of recommender systems. Social trust and social relation have been proven useful to improve the performance of recommendation. Based on the classic collaborative filtering technique, we propose a PCCTTF recommender method that takes the rating time of users, social trust among users, and item tags into consideration, then do the item recommending. Experimental results show that the PCCTTF method has better prediction accuracy than classical collaborative filtering technique and the state-of-the-art recommender methods, and can also effectively alleviate data sparsity and cold-start problem. Furthermore, the PCCTTF method has better performance than all the compared methods while counting against shilling attacks.
基金Supported by the National Natural Science Foundation of China(61063039)Project of Guangxi Key Laboratory of Trusted Software(kx201202)
文摘Inferring unknown social trust relations attracts increasing attention in recent years. However, social trust, as a social concept, is intrinsically dynamic, and exploiting temporal dynamics provides challenges and opportunities for social trust prediction. In this paper, we investigate social trust prediction by exploiting temporal dynamics. In particular, we model the dynamics of user preferences in two principled ways. The first one focuses on temporal weight; the second one targets temporal smoothness. By incorporating these two types of temporal dynamics into traditional matrix factorization based social trust prediction model, two extended social trust prediction models are proposed and the cor- responding algorithms to solve the models are designed too. We conduct experiments on a real-world dataset and the results dem- onstrate the effectiveness of our proposed new models. Further experiments are also conducted to understand the importance of temporal dynamics in social trust prediction.
文摘With the rapid development of social networks, there is a focus on marketing strategies and business models that are based on social media. In the academic world, scholars believe that online trust is a key factor contributing to online purchasing behavior. This article explored several factors in social media trust and verified the moderating role of offline familiarity by using relevant research on online trust in conjunction with a structure equation model. The results show that independent variables such as reputation, SNS interaction, information quality, reciprocity, satisfaction and shared values have a positive influence on trust, whereas perceived similarity does not, and information quality is the most important factor. In addition, offline familiarity significantly moderates the relations between information quality, reciprocity, reputation, shared values and social media trust. This information is important to assist companies in developing an effective social network marketing strategy.
文摘Recent years we have witnessed the rapid growth of social commerce in China, but many users are not willing to trust and use social commerce. So improving consumers’ trust and purchase intention has become a crucial factor in the success of social commerce. Business factors, environment factors and social factors including twelve secondary indexes build up a social commerce trust evaluation model. Questionnaires are handed out to collect twelve secondary indexes scores as input of BP neural network and composite score of trust as output. Model simulation shows that both training samples and test samples have low level of average error and standard deviation, which certify that the model has good stability and it is a good method for evaluating social commerce trust.
