Increase User Trust Research Articles

Development of a method for refining recommendations using temporal knowledge in individual insurance tasks

The subject of the research is the process of constructing recommendations in individual insurance tasks. The aim is to develop an approach for building recommendations in personalized insurance projects using temporal knowledge to adapt proposed insurance plans according to client behavior on insurance company websites. Objectives: to structure temporal rules considering the peculiarities of the online insurance recommendation process; to develop a method for constructing recommendations for insurance product selection using temporal knowledge. Conclusions: temporal rules have been structured for the task of building recommendations in personalized insurance projects. These rules generalize behavior in relative time for multiple users, enabling the generalization of user action sequences. A method for constructing online recommendations using temporal knowledge has been developed. The method consists of two phases: temporal knowledge formation and recommendation construction. The first phase is performed offline and is designed to form a base of temporal rules that generalize knowledge about user behavior. The second phase is executed online and involves refining and further utilizing temporal rules to adapt recommendations according to current user behavior. The method enables prompt adaptation of recommendations considering the user's current actions, creating conditions for increasing user trust in the recommender system's proposals.

Efektivitas Strategi Pemasaran dalam Membangun Kepercayaan Pengguna Pada BKKBISA

In maintaining the growth of a business, efforts are needed to develop effective marketing strategies including digital marketing with the marketing mix method and strategies in increasing user trust. This study aims to determine the effectiveness of marketing strategies in building BKKBISA user trust. The research method used was a qualitative approach, data collection techniques were carried out by interview, observation, and documentation with the director of PT TNR Solution Indonesia, BKKBISA users, and HR. Sinergia (BKKBISA partnership). The results of this study indicate that the marketing strategy carried out by BKKBISA in building user trust is with product strategy, price, place, promotion, people, process, and physical evidence. In addition, stratregies are also used in increasing consumer confidence, such as ability, benevolance, and integrity. From the application of these strategies, it was found that the marketing strategy carried out was effective, this was evidenced by the increase in the number of BKKBISA users from before, namely from 516,861 users in early November 2023 to 546,539 users in December 2023. This increase has a good effect on the development of the company

CONSTRUCTION OF PROBABILISTIC CAUSAL RELATIONSHIPS BETWEEN EQUIVALENCE CLASSES OF DATA IN AN INTELLIGENT INFORMATION SYSTEM

The subject of this research is the processes involved in generating explanations for decision-making in artificial intelligence systems. Explanations in such systems enable the decision-making process to be transparent and comprehensible for the user, thereby increasing user trust in the obtained results. The aim of this work is to develop an approach for constructing a probabilistic causal explanation model that takes into account the equivalence classes of input, intermediate, and resulting data. Solving this problem creates conditions for building explanations in the form of causal relationships based on the available information about the properties of input data as well as the properties of the results obtained in the artificial intelligence system. To achieve this aim, the following tasks are addressed: developing a causal dependency model between the equivalence classes of input and output data; developing methods for constructing equivalence classes of data in the decision-making process and a method for constructing causal explanations. A probabilistic model of causal dependency is proposed, which includes a causal relationship between the equivalence classes of input or intermediate and resulting data obtained during the decision-making process in the artificial intelligence system. This relationship considers the estimates of the possibility and necessity of such a dependency. The model creates conditions for explaining the possible causes of the obtained decision. A set of methods for constructing equivalence classes of data in the decision-making process and for constructing causal explanations is proposed, establishing a causal relationship between the equivalence classes. When constructing equivalence classes, relations of mandatory and optional data refinement, requirements or exclusions of data, as well as data conjunctions, are established. When constructing causal explanations, the possibility and limitations of the necessity of such a dependency are calculated, allowing explanations to be built based on the available information about the obtained decisions and the input and intermediate data used to form these decisions.

The Impact of Performance Expectancy, Workload, Risk, and Satisfaction on Trust in ChatGPT: Cross-Sectional Survey Analysis.

ChatGPT (OpenAI) is a powerful tool for a wide range of tasks, from entertainment and creativity to health care queries. There are potential risks and benefits associated with this technology. In the discourse concerning the deployment of ChatGPT and similar large language models, it is sensible to recommend their use primarily for tasks a human user can execute accurately. As we transition into the subsequent phase of ChatGPT deployment, establishing realistic performance expectations and understanding users' perceptions of risk associated with its use are crucial in determining the successful integration of this artificial intelligence (AI) technology. The aim of the study is to explore how perceived workload, satisfaction, performance expectancy, and risk-benefit perception influence users' trust in ChatGPT. A semistructured, web-based survey was conducted with 607 adults in the United States who actively use ChatGPT. The survey questions were adapted from constructs used in various models and theories such as the technology acceptance model, the theory of planned behavior, the unified theory of acceptance and use of technology, and research on trust and security in digital environments. To test our hypotheses and structural model, we used the partial least squares structural equation modeling method, a widely used approach for multivariate analysis. A total of 607 people responded to our survey. A significant portion of the participants held at least a high school diploma (n=204, 33.6%), and the majority had a bachelor's degree (n=262, 43.1%). The primary motivations for participants to use ChatGPT were for acquiring information (n=219, 36.1%), amusement (n=203, 33.4%), and addressing problems (n=135, 22.2%). Some participants used it for health-related inquiries (n=44, 7.2%), while a few others (n=6, 1%) used it for miscellaneous activities such as brainstorming, grammar verification, and blog content creation. Our model explained 64.6% of the variance in trust. Our analysis indicated a significant relationship between (1) workload and satisfaction, (2) trust and satisfaction, (3) performance expectations and trust, and (4) risk-benefit perception and trust. The findings underscore the importance of ensuring user-friendly design and functionality in AI-based applications to reduce workload and enhance user satisfaction, thereby increasing user trust. Future research should further explore the relationship between risk-benefit perception and trust in the context of AI chatbots.

Effects of Explanation Types on User Satisfaction and Performance in Human-agent Teams

Automated agents, with rapidly increasing capabilities and ease of deployment, will assume more key and decisive roles in our societies. We will encounter and work together with such agents in diverse domains and even in peer roles. To be trusted and for seamless coordination, these agents would be expected and required to explain their decision making, behaviors, and recommendations. We are interested in developing mechanisms that can be used by human-agent teams to maximally leverage relative strengths of human and automated reasoners. We are interested in ad hoc teams in which team members start to collaborate, often to respond to emergencies or short-term opportunities, without significant prior knowledge about each other. In this study, we use virtual ad hoc teams, consisting of a human and an agent, collaborating over a few episodes where each episode requires them to complete a set of tasks chosen from available task types. Team members are initially unaware of the capabilities of their partners for the available task types, and the agent task allocator must adapt the allocation process to maximize team performance. It is important in collaborative teams of humans and agents to establish user confidence and satisfaction, as well as to produce effective team performance. Explanations can increase user trust in agent team members and in team decisions. The focus of this paper is on analyzing how explanations of task allocation decisions can influence both user performance and the human workers’ perspective, including factors such as motivation and satisfaction. We evaluate different types of explanation, such as positive, strength-based explanations and negative, weakness-based explanations, to understand (a) how satisfaction and performance are improved when explanations are presented, and (b) how factors such as confidence, understandability, motivation, and explanatory power correlate with satisfaction and performance. We run experiments on the CHATboard platform that allows virtual collaboration over multiple episodes of task assignments, with MTurk workers. We present our analysis of the results and conclusions related to our research hypotheses.

Thesis Summary: Operationalizing User-Inclusive Transparency in Artificial Intelligence Systems

Artificial intelligence system architects can increase user trust by designing systems that are inherently transparent. We propose the idea of representing an AI system as an amalgamation of the AI Model (algorithms), data (input and output, including outcomes), and the user interface with visual interpretations (e.g. graphs, Venn diagrams). By designing human controls and feedback mechanisms for AI systems that allow users to exert control over them we can integrate transparency into existing user interfaces. Our plan is to design prototypes of transparent user interfaces for AI systems using well-known usability principles. By conducting surveys we will study their impact to see if these principles help the user to work with the AI system with confidence and if the user perceives the system to be adequately transparent.

Revolutionizing collaborative auditing: A dynamic blockchain-based cloud storage framework for data updates and assurance

Effective data management has arisen as a major concern in today’s era of ubiquitous data generation from a plethora of intelligent gadgets. While data proliferation promises unparalleled benefits, it imposes significant storage and computing constraints, particularly on end-users with limited capabilities. To solve these difficulties, this article investigates the confluence of cloud storage, blockchain technology, public auditing, reputation systems, and dynamic auditing. Because of their low-cost data storage and processing capabilities, cloud computing services have grown in popularity, leading customers to embrace data outsourcing to reduce local administrative overhead. This study digs into a novel paradigm for ensuring the integrity and security of data stored in cloud environments using blockchain technology. Integrating public auditing systems enables visible and verifiable data audits, ensuring consumers of data trustworthiness. A reputation system is also included to build trust among cloud service providers and users, improving the overall trustworthiness of the ecosystem. The suggested system also includes dynamic auditing, which allows for real-time changes and data verification, reacting to the changing nature of cloud-stored information. This study provides a thorough examination of the architectural components, techniques, and protocols used in this novel approach. We illustrate the feasibility and usefulness of our approach in ensuring data integrity, security, and reliability in cloud storage systems through empirical analysis and case studies. The findings show the potential benefits of this integrated strategy to solving the issues posed by the modern digital landscape’s tremendous proliferation of data. Through the synergistic integration of cloud storage, blockchain technology, public auditing, reputation systems, and dynamic auditing, this research provides a holistic solution for managing data in the cloud while ensuring data integrity, security, and trust. This comprehensive strategy lays the way for a more robust and dependable cloud data management ecosystem, increasing user trust in cloud-based services.

Sentiment Analysis of Islamic Fintech: Uncovering the Pulse of Twitter Post-Covid-19

Twitter user attitude towards sharia fintech research provides an intriguing glimpse into perceptions and responses to sharia-based financial innovation. According to an analysis of 2,131 tweets, the bulk of attitudes were neutral (80.8%), followed by positive sentiments (16.2%), and the least were negative sentiments (3%). These findings paint an intriguing picture of how users react to sharia-compliant fintech concepts. The predominance of neutral attitude may indicate that the majority of Twitter users are not yet completely engaged with or have a neutral opinion of sharia fintech. This could be due to a lack of knowledge about the concept or a lack of direct connection with Islamic financial channels. The presence of strong positive sentiment, on the other hand, suggests interest in and support for this endeavour. There may be a view that sharia fintech can give a more ethical and in line with Islamic financial principles alternative. The unfavourable mood that has evolved, however, cannot be disregarded. Opinions opposing sharia fintech can stem from a variety of sources, including questions about its compatibility with authentic sharia principles or concerns about the hazards and security of its use. It is critical to understand the causes of unfavourable attitude and to improve education and transparency about sharia fintech. Overcoming questions and fears may necessitate an all-inclusive strategy to regulation, outreach, and risk management. In this approach, it can increase user trust and knowledge of the existence and benefits of sharia fintech in the current financial environment.

Sistem Informasi Pemesanan Jasa Fotografer Berbasis Web Pada Studio Fotograferku

Studio Fotograferku is a company engaged in photography services. In the current digital era, the need for photography services continues to increase along with the growth of the creative industry and social media. However, the process of finding and ordering photographers often takes a long time and is less efficient. Seeing this potential, we developed a web-based photographer ordering information system. This system aims to make it easier for customers to search, compare, and order photographer services according to their needs and preferences. This system provides a photographer search feature based on category, location, price, and portfolio. Custome can view profiles, works and reviews of available photographers. In addition, this system is equipped with a calendar feature that allows customers to see the availability of dates from photographers and place orders directly through the platform. To increase user trust, the system is also equipped with a rating and review system that can assist customers in making decisions. From the photographer's perspective, they can register themselves, manage their profile, upload their portfolio, and schedule their activities. This system is also equipped with a notification feature to inform photographers about new orders or changes to existing orders. This web-based photographer ordering information system is expected to be a bridge between photographers and customers, increase efficiency in the ordering process, and expand market reach for photographers.

Recommendations with Benefits: Exploring Explanations in Information Sharing Recommender Systems for Temporary Teams

Increased use of collaborative technologies and agile teamwork models has led to a greater need for temporary teams. Unfortunately, they lack the normal team formation processes that traditional teams use. Information sharing recommender systems can be used to share information about team members amongst the team; however, these systems rely on the team members themselves to disclose valuable information. While prior research has shown that an effective way to encourage user disclosure is through explanations to the user about what benefits they will gain from disclosure, the timing of such explanations has yet to be consideblack. In a between-subjects study with 150 participants, we assessed the content and timing of explanations on levels of disclosure in temporary teams. Our results indicate that providing benefit-related explanations during the time of disclosure can increase user disclosure, and providing benefit-related explanations during the recommendation process can increase user trust in the system. These results provide important design implications for teams and the HCI community.

Implementasi Electronic-Know Your Customer pada aplikasi Fintech untuk meningkatkan keamanan akun user

The information technology development can make financial transactions easier, but the security of Fintech applications such as Bangbeli is a critical issue that can be addressed through the implementation of the Electronic-Know Your Customer (E-KYC) feature to increase user trust. The aim of this research is to apply E-KYC technology as an additional feature in the Bangbeli application at PT. Doa Anak Digital. The approach used is the agile development method which consists of six stages, including requirements, design, development, testing, implementation and review. This research uses a qualitative approach, involving interviews with selected sources to ensure that the E-KYC feature can improve user account security. Implementation includes coding and implementing E-KYC in the Bangbeli application, followed by testing using the black box method to identify errors or bugs in the E-KYC system. The results of the research show conformity with the initial design, with features that run according to their function. As a result of interviews with users and Bangbeli regarding the implementation of the E-KYC feature, a number of feedback were obtained including an easy to understand flow, innovations that increase security, confidence in sending personal data, and the impression that this platform cares about user security.

Explainable autonomous robots in continuous state space based on graph-structured world model

Autonomous robots using deep reinforcement learning have demonstrated superior capabilities on relatively simple specific tasks, but they often lack high-level, abstract planning capabilities when faced with complex, long-horizon tasks. Even if the autonomous robot successfully achieves long-horizon goals, users find it difficult to trust their decision-making process. To increase user trust in the decision-making process when an autonomous robot executes a long-horizon task, this paper proposes an algorithm that empowers the autonomous agent to explain to users the transition from the current state to the target state in a continuous state space, as well as to explain the errors in user estimates. A framework that uses a graph-based world model is proposed to identify important nodes and reachability between nodes in the decision-making process; based on these nodes and reachability, the model generates the required explanations. To validate our proposed method's ability to generate long-horizon plans and explanations, we conducted experiments in PointMaze environments. Our simulation results confirm the effectiveness of our approach in generating reliable world models for long-horizon tasks. Moreover, our explanations, based on these world models, significantly enhance the user's understanding of the autonomous robotic decision-making process and the system's capabilities and limitations.

A Fully Integrated Pipeline for Visual Carotid Morphology Analysis

AbstractAnalyzing stenoses of the internal carotids – local constrictions of the artery – is a critical clinical task in cardiovascular disease treatment and prevention. For this purpose, we propose a self‐contained pipeline for the visual analysis of carotid artery geometries. The only inputs are computed tomography angiography (CTA) scans, which are already recorded in clinical routine. We show how integrated model extraction and visualization can help to efficiently detect stenoses and we provide means for automatic, highly accurate stenosis degree computation. We directly connect multiple sophisticated processing stages, including a neural prediction network for lumen and plaque segmentation and automatic global diameter computation. We enable interactive and retrospective user control over the processing stages. Our aims are to increase user trust by making the underlying data validatable on the fly, to decrease adoption costs by minimizing external dependencies, and to optimize scalability by streamlining the data processing. We use interactive visualizations for data inspection and adaption to guide the user through the processing stages. The framework was developed and evaluated in close collaboration with radiologists and neurologists. It has been used to extract and analyze over 100 carotid bifurcation geometries and is built with a modular architecture, available as an extendable open‐source platform.

Estimating and Evaluating the Uncertainty of Rating Predictions and Top-n Recommendations in Recommender Systems

Uncertainty is a characteristic of every data-driven application, including recommender systems. The quantification of uncertainty can be key to increasing user trust in recommendations or choosing which recommendations should be accompanied by an explanation; uncertainty estimates can be used to accomplish recommender tasks such as active learning and co-training. Many uncertainty estimators are available, but to date, the literature has lacked a comprehensive survey and a detailed comparison. In this article, we fulfill these needs. We review the existing methods for uncertainty estimation and metrics for evaluating uncertainty estimates, while also proposing some estimation methods and evaluation metrics of our own. Using two datasets, we compare the methods using the evaluation metrics that we describe, and we discuss their strengths and potential issues. The goal of this work is to provide a foundation to the field of uncertainty estimation in recommender systems, on which further research can be built.

Personal data and personalisation in media: experts’ perceptions of value, benefits, and risks

PurposeMedia users daily exchange personal data for “free” personalised media. Is this a fair trade, or user “exploitation”? Do personalisation benefits outweigh privacy risks?Design/methodology/approachThis study surveyed experts in three consecutive online rounds (e-Delphi). The authors explored personal data processing value for media, personalisation relevance, benefits and risks for users. The authors scrutinised the value-exchange between media and users and determined whether media communicate transparently, or use “dark patterns” to obtain more personal data.FindingsCommunication to users must be clear, correct and concise (prevent user deception). Experts disagree on “payment” with personal data for “free” personalised media. This study discerned obstacles and solutions to substantially balance the interests of media and users (fair value exchange). Personal data processing must be transparent, profitable to media and users. Media can agree “sector-wide” on personalisation transparency. Fair, secure and transparent information disclosure to media is possible through shared responsibility and effort.Originality/valueThis study’s innovative contribution is threefold: Firstly, focus on professional stakeholders’ opinion in the value network. Secondly, recommendations to clearly communicate personalised media value, benefits and risks to users. This allows media to create codes of conduct that increase user trust. Thirdly, expanding literature explaining how media realise personal data value, deal with stakeholder interests and position themselves in the data processing debate. This research improves understanding of personal data value, processing benefits and potential risks in a regional context and European regulatory framework.

Design and Implementation of a Review Management System in the Field of Medicine and Health

Introduction: The increasing growth of science and specialized articles in various fields, especially in health and treatment, has faced researchers with the challenge of keeping up with this progress. Review research provides researchers with the possibility to gain access to an acceptable and categorized amount of information by spending less time and money and helps them reach the original essence of the research. However, carrying out such research is very difficult because it requires deep knowledge of the specific field as well as reviewing several studies to find and gather appropriate information. By developing a system, we intended to help researchers in the process of categorizing, extracting, and aggregating data and findings. This study aimed to design and implement a database management system to review studies in the field of medicine and health . Method: This practical study was done in 2022, and associated with software development. The system was developed in three stages as follows: 1) analysis, 2) implementation, and 3) testing and troubleshooting. This system is implemented using the frameworks based on JavaScript, Angular on the front -end side and Node JS on the back -end side. In the final stage, the system was tested using actual research, and possible defects and problems were detected and solved; finally, the system was handed over to the research team to conduct new research. Results: In the process of testing the system, we found that by using this system, the accuracy of the information entry process can be greatly increased, and the analysis of the results can be done online with high speed and accuracy . Conclusion: The results of the present analysis can help policy makers for appropriate legislation and business managers to solve identified problems and increase user trust and satisfaction .

It is Not “Accuracy vs. Explainability”—We Need Both for Trustworthy AI Systems

We are witnessing the emergence of an “AI economy and society” where AI technologies and applications are increasingly impacting health care, business, transportation, defense and many aspects of everyday life. Many successes have been reported where AI systems even surpassed the accuracy of human experts. However, AI systems may produce errors, can exhibit bias, may be sensitive to noise in the data, and often lack technical and judicial transparency resulting in reduction in trust and challenges to their adoption. These recent shortcomings and concerns have been documented in both the scientific and general press such as accidents with self-driving cars, biases in healthcare or hiring and face recognition systems for people of color, and seemingly correct decisions later found to be made due to wrong reasons etc. This has resulted in the emergence of many government and regulatory initiatives requiring trustworthy and ethical AI to provide accuracy and robustness, some form of explainability, human control and oversight, elimination of bias, judicial transparency and safety. The challenges in delivery of trustworthy AI systems have motivated intense research on explainable AI systems (XAI). The original aim of XAI is to provide human understandable information of how AI systems make their decisions in order to increase user trust. In this paper we first very briefly summarize current XAI work and then challenge the recent arguments that present “accuracy vs. explainability” as being mutually exclusive and for focusing mainly on deep learning with its limited XAI capabilities. We then present our recommendations for the broad use of XAI in all stages of delivery of high stakes trustworthy AI systems, e.g., development; validation/certification; and trustworthy production and maintenance.

Effects of government electronic service quality on citizen satisfaction with integrated service delivery in urban areas

In the effort to achieve good and effective public services to gain public satisfaction and trust, the use of public electronic services (e-services) is expected to improve service quality. Public e-services offer better options for processing digital data. With increasing internet access, the use of e-services as a vehicle for delivering various public services is a promising development in the public sector. By maintaining and improving service quality, especially in the public sector, the use of internet/online services or public e-services will make users feel more satisfied and increase public trust. This study aims to determine the effect of electronic service quality on citizen satisfaction with public e-services. A quantitative research methodology was adopted. Data was collected by distributing online questionnaires to users of public e-service applications, totaling 150 respondents in Bandung, Indonesia. The sampling technique used was a purposive, non-probability-based sampling method. The data analysis technique was Structural Equation Modeling (SEM) using Smart software. The test results show that information quality, data security, and data privacy have a significant effect on e-service quality, which, in turn, has implications for citizen satisfaction. Based on the results of this study, we advise public e-services institutions to maintain and improve the quality of e-services by improving information quality, data security, and data privacy to increase user trust in public e-services.

AKUNTANSI SYARIAH SEBAGAI MEDIA INFORMASI DALAM MENINGKATKAN KEPERCAYAAN PENGGUNA

This paper aims to find out about the usefulness of Islamic accounting as a medium in increasing user trust. Accounting is a medium of information used to identify, collect, record, all transactions in a company that is used as a tool to inform users of a company's financial statements. The recording of financial statements is the most important thing in a company because it has a significant influence on the development of the company. The method used in this study is a literature study method, which is to collect all references related to Islamic accounting as a medium of information in increasing user trust. The results of this study indicate that sharia accounting information is a solution in deconstructing conventional accounting that has been used so far that puts forward sharia principles and has great responsibility, especially in moral issues.

Explainable artificial intelligence for photovoltaic fault detection: A comparison of instruments

Faults in photovoltaic arrays are known to cause severe energy losses. Data-driven models based on machine learning have been developed to automatically detect and diagnose such faults. A majority of the models proposed in the literature are based on artificial neural networks, which unfortunately represent black-boxes, hindering user interpretation of the models’ results. Since the energy sector is a critical infrastructure, the security of energy supply could be threatened by the deployment of such models. This study implements explainable artificial intelligence (XAI) techniques to extract explanations from a multi-layer perceptron (MLP) model for photovoltaic fault detection, with the aim of shedding some light on the behavior of XAI techniques in this context. Three techniques were implemented: Shapley Additive Explanations (SHAP), Anchors and Diverse Counterfactual Explanations (DiCE), each representing a distinct class of local explainability techniques used to explain predictions. For a model with 99.11% accuracy, results show that SHAP explanations are largely in line with domain knowledge, demonstrating their usefulness to generate valuable insights on model behavior which could potentially increase user trust in the model. Compared to Anchors and DiCE, SHAP demonstrated a higher degree of stability and consistency.