Increase User Trust Research Articles

Increasing User Trust in Optimisation through Feedback and Interaction

User trust plays a key role in determining whether autonomous computer applications are relied upon. It will play a key role in the acceptance of emerging AI applications such as optimisation. Two important factors known to affect trust are system transparency, i.e., how well the user understands how the system works, and system performance. However, in the case of optimisation, it is difficult for the end-user to understand the underlying algorithms or to judge the quality of the solution. Through two controlled user studies, we explore whether the user is better able to calibrate their trust in the system when: (a) They are provided feedback on the system operation in the form of visualisation of intermediate solutions and their quality; (b) They can interactively explore the solution space by modifying the solution returned by the system. We found that showing intermediate solutions can lead to over-trust, while interactive exploration leads to more accurately calibrated trust.

The Factors Affecting Digital Bank Services Adoption Using Trust as Mediating Variable

The rise of the internet in Indonesia have pushed businesses to speed up the digital transformation of the financial sector, including banking and financial technology. This has led to changes in the structure of the economy and how businesses compete and collaborate. A Digital Bank is one of the business model innovations because it relies on a digital ecosystem. It is important for people to have trust in digital bank services because this digital bank doesn't have a branch office to provide its services, and there aren't adequate regulations to protect data or security system. This study aims to determine what factors influence customers' adoption of a digital bank by analyzing the effects of perceived security, perceived enjoyment, financial literacy, and brand image on the adoption of digital payment services using trust as a mediating variable. This research was conducted using quantitative methods with Digital Bank users as the respondents. The results of the study indicate that perceived security, perceived enjoyment, financial literacy, and brand image have a positive and significant influence on the adoption of digital bank services in Indonesia, where trust variable has the greatest effects on the adoption of Digital Bank services. Therefore, Digital Bank companies need to increase user trust by increasing IT resources, developing attractive and useful digital platforms, and building a good image of the Digital Bank industry as a trusted service.

A survey on artificial intelligence in histopathology image analysis

AbstractThe increasing adoption of the whole slide image (WSI) technology in histopathology has dramatically transformed pathologists' workflow and allowed the use of computer systems in histopathology analysis. Extensive research in Artificial Intelligence (AI) with a huge progress has been conducted resulting in efficient, effective, and robust algorithms for several applications including cancer diagnosis, prognosis, and treatment. These algorithms offer highly accurate predictions but lack transparency, understandability, and actionability. Thus, explainable artificial intelligence (XAI) techniques are needed not only to understand the mechanism behind the decisions made by AI methods and increase user trust but also to broaden the use of AI algorithms in the clinical setting. From the survey of over 150 papers, we explore different AI algorithms that have been applied and contributed to the histopathology image analysis workflow. We first address the workflow of the histopathological process. We present an overview of various learning‐based, XAI, and actionable techniques relevant to deep learning methods in histopathological imaging. We also address the evaluation of XAI methods and the need to ensure their reliability on the field.This article is categorized under: Application Areas > Health Care

Edge-cloud resource federation for sustainable cities

As cloud computing becomes the dominant mechanism for delivery of electronic services, significant recent effort has focused on certifying cloud services to ensure their compliance with security and privacy standards (such as GDPR). Assessing the benefit of using a particular cloud service, especially if such a service is being offered by providers that may be new to the cloud marketplace, remains a challenge. Cloud Security Alliance (CSA) provides a certification approach that associates a ranking to providers based on their capability assessment using a “cloud control matrix”. A provider can make a self-assessment, or an assessment can be undertaken by a third party. The intention is to increase user trust in a provider based on their rating using this methodology. This work investigates whether a similar certification methodology can be applied to edge resources, especially if these edge resources are combined with cloud services in a “smart cities” context. Can a CSA-like approach also be used to increase trust in use of edge resources? How would the CSA methodology need to change to support this type of assessment, and how useful is such an approach likely to be in practice? We propose a risk assessment methodology that can be used to address these concerns, and evaluate it in a practical application using both edge and cloud computing resources. • “ Smartainability ” - a strategy to assess how sustainable smart cities are as a result of smart technology. • Smart services must ensure their compliance with security and privacy standards (such as GDPR). • Cloud-Edge-IoT resources differ in their capability, security and availability profiles — making reliance on them risk prone. • Trust can be established across an ensemble of “smart” devices and clouds to support “risk-informed” interventions.

Review on Interpretable Machine Learning in Smart Grid

In recent years, machine learning, especially deep learning, has developed rapidly and has shown remarkable performance in many tasks of the smart grid field. The representation ability of machine learning algorithms is greatly improved, but with the increase of model complexity, the interpretability of machine learning algorithms is worse. The smart grid is a critical infrastructure area, so machine learning models involving it must be interpretable in order to increase user trust and improve system reliability. Unfortunately, the black-box nature of most machine learning models remains unresolved, and many decisions of intelligent systems still lack explanation. In this paper, we elaborate on the definition, motivations, properties, and classification of interpretability. In addition, we review the relevant literature addressing interpretability for smart grid applications. Finally, we discuss the future research directions of interpretable machine learning in the smart grid.

Fully automated global longitudinal strain assessment using artificial intelligence developed and validated by a UK-wide echocardiography expert collaborative

Abstract Background Left ventricular longitudinal strain has been reported to deliver reproducibility, sensitivity and prognostic value over and above ejection fraction. However, it currently relies on uninspectable proprietary algorithms and suffers from a lack of widespread clinical use. Uptake may be improved by increasing user trust through greater transparency. Purpose We therefore developed a machine-learning based method, trained, and validated with accredited experts from our AI Echocardiography Collaborative. We make the dataset, code, and trained network freely available under an open-source license. Methods AI enables strain to be calculated without relying on speckle tracking by directly locating key points and borders across frames. Strain can then be calculated as the fractional shortening of the left ventricular perimeter. We first curated a dataset of 7523 images, including 2587 apical four chamber, each labelled by a single expert from our collaboration of 17 hospitals, using our online platform (Figure 1). Using both this dataset and a semi-supervised approach, we trained a 3d convolutional neural network to identify the annulus, apex, and the endocardial border throughout the cardiac cycle. Separately, we constructed an external validation dataset of 100 apical 4 chamber video-loops. The systolic and diastolic frame were identified, and each image was separately labelled by 11 experts. From these labels we then derived the expert consensus strain for each of the 100 video loops. These experts also ordered all 100 echocardiograms by their visual grading of left ventricular longitudinal function. Finally, a single expert calculated strain using two different proprietary commercial packages (A and B). Results Consensus strain measurements (obtained by averaging individual assessments by the 11 experts) across the 100 cases ranged from −4% to −27%, with strong correlations with the individual experts and machine methods (Figure 2). Using each cases' consensus across experts as the gold standard, median error from consensus was 3.1% for individual experts, 3.4% for Propriety A, 2.6% for Proprietary B, 2.6% for our AI. Using the visual grading of longitudinal strain as the reference, the 11 individual experts and 4 machine methods each showed significant correlation: coefficients ranged from 0.55 to 0.69 for experts, and for Proprietary A was 0.68, Proprietary B 0.69, and our AI 0.69. Conclusions Our open-source, vendor-independent AI-based strain measure automatically produces values that agree with expert consensus, as strongly as the individual experts do. It also agrees with the subjective visual ranking by longitudinal function. Our open-source AI strain performs at least as well as closed-source speckle-based approaches, and may enable increased clinical and research use of longitudinal strain. Funding Acknowledgement Type of funding sources: Public grant(s) – National budget only. Main funding source(s): NIHR Imperial BRC ITMAT.Dr Howard was additionally funded by Wellcome. Figure 1. Collaborative online platformFigure 2. Correlations between strain methods

Integrating an information systems success model with perceived privacy, perceived security, and trust: the moderating role of Facebook addiction

PurposeThe purpose of this study is to investigate the influence of perceived security, perceived privacy, and satisfaction on Facebook user continuance intention. In addition, the serial multiple mediating effects of trust and satisfaction on the relationship between continuance intention and Facebook determinants are explored. This study also investigates the moderating role of Facebook addiction on the relationship between satisfaction and continuance intention. Design/methodology/approachTo achieve the study aims, an online survey was conducted among university undergraduate students. Data were collected from 450 voluntary participants. The statistical analysis was performed using SPSS and PROCESS macro models. FindingsThe study results confirm that perceived privacy and satisfaction have significant impacts on Facebook continuance intention. The path analysis results confirm the full mediating roles of trust and satisfaction in the relationship between perceived security and continuance intention. Furthermore, the moderating role of Facebook addiction on the relationship between satisfaction and continuance intention is confirmed. Practical implicationsThe results of this study offer insights to Facebook managers and policymakers regarding the treatment of and intervention in Facebook continuance intention. The results disclose the critical role of users' satisfaction and perceived privacy in influencing Facebook users' continuance intention for Facebook managers and policymakers. This confirms that Facebook managers and policymakers must maintain user privacy to increase the level of user satisfaction and continuance intention. Furthermore, they must ensure that the requisite security mechanisms are in place to increase user trust and satisfaction that influence users’ continuance intention. Originality/valueThis research integrated perceived privacy, perceived security, and trust with an Information System (IS) success model. This is the first study to investigate the serial mediating effects of trust and satisfaction on the determinants of Facebook continuance intention. The moderating effects of Facebook addiction on the relationship between satisfaction and continuance intention are also examined. The study results make important contributions to Facebook continuance intention research and advance scholarship into aspects of undergraduate students’ continuance intention in the context of Facebook.

Augmenting the technology acceptance model with trust model for the initial adoption of a blockchain-based system.

BackgroundIn the collaborative business environment, blockchain coupled with smart contract removes the reliance on a central system and offers data integrity which is crucial when the transacting parties rely on the shared data. The acceptance of such blockchain-based systems is necessary for the continued use of the services. Despite many extensive studies evaluating the performance of blockchain-based systems, few have focused on users’ acceptance of real-life applications.ObjectiveThe main objective of this research is to evaluate the user acceptance of a real-life blockchain-based system (BBS) by observing various latent variables affecting the development of users’ attitudes and intention to use the system. It also aims to uncover the dimensions and role of trust, security and privacy alongside the primary Technology Acceptance Model (TAM)-based predictors and their causal relationship with the users’ behavior to adopt such BBS.MethodsWe tested the augmented TAM with Trust Model on a BBS that comprises two subsystems: a Shopping Cart System (SCS), a system oriented towards end-users and a Data Sharing System (DSS), a system oriented towards system administrators. We set research questions and hypotheses, and conducted online surveys by requesting each participant to respond to the questionnaire after using the respective system. The main study comprises two separate sub-studies: the first study was performed on SCS and the second on DSS. Furthermore, each study data comprises initial pre-test and post-test data scores. We analyzed the research model with partial least square structural equation modelling.ResultsThe empirical study validates our research model and supports most of the research hypotheses. Based on our findings, we deduce that TAM-based predictors and trust constructs cannot be applied uniformly to BBS. Depending on the specifics of the BBS, the relationships between perceived trust antecedents and attitudes towards the system might change. For SCS, trust is the strongest determinant of attitudes towards system, while DSS has perceived privacy as the strongest determinant of attitudes towards system. Quality of system shows the strongest total effect on intention to use SCS, while perceived usefulness has the strongest total effect on intention to use DSS. Trust has a positive significant effect on users’ attitudes towards both BSS, while security does not have any significant effect on users’ attitudes toward BBS. In SCS, privacy positively affects trust, but security has no significant effect on trust, whereas, in DSS, both privacy and security have significant effects on trust. In both BBS, trust has a moderating effect on privacy that correlates with attitudes towards BBS, whereas security does not have any mediating role between privacy and attitudes towards BBS. Hence, we recommend that while developing BBS, particular attention should be paid to increasing user trust and perceived privacy.

Thinking Technology as Human: Affordances, Technology Features, and Egocentric Biases in Technology Anthropomorphism

Advanced information technologies (ITs) are increasingly assuming tasks that have previously required human capabilities, such as learning and judgment. What drives this technology anthropomorphism (TA), or the attribution of humanlike characteristics to IT? What is it about users, IT, and their interactions that influences the extent to which people think of technology as humanlike? While TA can have positive effects, such as increasing user trust in technology, what are the negative consequences of TA? To provide a framework for addressing these questions, we advance a theory of TA that integrates the general three-factor anthropomorphism theory in social and cognitive psychology with the needs-affordances-features perspective from the information systems (IS) literature. The theory we construct helps to explain and predict which technological features and affordances are likely: (1) to satisfy users’ psychological needs, and (2) to lead to TA. More importantly, we problematize some negative consequences of TA. Technology features and affordances contributing to TA can intensify users’ anchoring with their elicited agent knowledge and psychological needs and also can weaken the adjustment process in TA under cognitive load. The intensified anchoring and weakened adjustment processes increase egocentric biases that lead to negative consequences. Finally, we propose a research agenda for TA and egocentric biases.

Trusting the X in XAI: Effects of different types of explanations by a self-driving car on trust, explanation satisfaction and mental models

There is an increasing demand for opaque intelligent systems to explain themselves to humans, in order to increase user trust and the formation of adequate mental models. Previous research has shown effects of different types of explanations on user preferences and performance. However, this research has not addressed the differential effects of intentional and causal explanations on both users’ trust and mental models, nor has it employed multiple trust measurement scales at multiple points in time. In the current research, the effects of three types of explanations (causal, intentional, mixed) on trust development, mental models, and user satisfaction were investigated in the context of a self-driving car. Results showed that participants were least satisfied with causal explanations, that intentional explanations were most effective in establishing high levels of trust, and that mixed explanations led to the best functional understanding of the system and resulted in the least changes in trust over time.

The emergence of explainability of intelligent systems: Delivering explainable and personalized recommendations for energy efficiency

The recent advances in artificial intelligence namely in machine learning and deep learning, have boosted the performance of intelligent systems in several ways. This gave rise to human expectations, but also created the need for a deeper understanding of how intelligent systems think and decide. The concept of explainability appeared, in the extent of explaining the internal system mechanics in human terms. Recommendation systems are intelligent systems that support human decision making, and as such, they have to be explainable in order to increase user trust and improve the acceptance of recommendations. In this work, we focus on a context-aware recommendation system for energy efficiency and develop a mechanism for explainable and persuasive recommendations, which are personalized to user preferences and habits. The persuasive facts either emphasize on the economical saving prospects (Econ) or on a positive ecological impact (Eco) and explanations provide the reason for recommending an energy saving action. Based on a study conducted using a Telegram bot, different scenarios have been validated with actual data and human feedback. Current results show a total increase of 19\% on the recommendation acceptance ratio when both economical and ecological persuasive facts are employed. This revolutionary approach on recommendation systems, demonstrates how intelligent recommendations can effectively encourage energy saving behavior.

The influence of system transparency on trust: Evaluating interfaces in a highly automated vehicle

Previous studies indicate that, if an automated vehicle communicates its system status and intended behaviour, it could increase user trust and acceptance. However, it is still unclear what types of interfaces will better portray this type of information. The present study evaluated different configurations of screens comparing how they communicated the possible hazards in the environment (e.g. vulnerable road users), and vehicle behaviours (e.g. intended trajectory). These interfaces were presented in a fully automated vehicle tested by 25 participants in an indoor arena. Surveys and interviews measured trust, usability and experience after users were driven by an automated low-speed pod. Participants experienced four types of interfaces, from a simple journey tracker to a windscreen-wide augmented reality (AR) interface which overlays hazards highlighted in the environment and the trajectory of the vehicle. A combination of the survey and interview data showed a clear preference for the AR windscreen and an animated representation of the environment. The trust in the vehicle featuring these interfaces was significantly higher than pretrial measurements. However, some users questioned if they want to see this information all the time. One additional result was that some users felt motion sick when presented with the more engaging content. This paper provides recommendations for the design of interfaces with the potential to improve trust and user experience within highly automated vehicles.

Complex ecologies of trust in data practices and data-driven systems

ABSTRACT Trust in data practices and data-driven systems is widely seen as both important and elusive. A data trust deficit has been identified, to which proposed solutions are often localised or individualised, focusing either on what institutions can do to increase user trust in their data practices or on data management models that empower the individual user. Scholarship on trust often focuses on typologies of trust. This paper shifts the emphasis to those doing the trusting, by presenting findings from empirical research which explored user perspectives on the data practices of the BBC. These findings challenge the assumption that localised or individualised solutions can be effective. They also suggest that conceptualisations of trust in data practices need to account for the complex range of factors which come into play in relation to trust in data and so move beyond the production of typologies. In this paper, we propose the concept of ‘complex ecologies of trust’ as a way of addressing all of these issues.

A User-Friendly Privacy Framework for Users to Achieve Consents With Nearby BLE Devices

The deployment of IoT devices with significant data collection capabilities around the world raises concerns about user privacy. People are worried about ubiquitous IoT devices collecting and sharing their data with unknown parties without their awareness or consent. Currently, several governmental agencies have stated that IoT service providers should obtain user consent before collecting and using their personal data. However, to the best of our knowledge, there is no standard means for users to reach agreements on privacy practices for IoT applications. Among different types of IoT applications, this paper focuses on the scenario in which people use their personal smartphones to access nearby IoT devices via Bluetooth Low Energy (BLE). To address the privacy issue in the scenario, this paper proposes a privacy preferences expression framework for BLE-based applications named PrivacyBat. The framework defines specifications for users to achieve agreements on privacy practices with nearby BLE devices. In addition, this framework provides guidelines for a device to process user requests according to the agreement. To demonstrate how the framework operates, this paper further provides a proof of concept implementation. As the proposed framework can improve the privacy policy agreement process in IoT applications, this paper can hopefully contribute to increasing user trust in IoT applications.

ASArP: Automated Security Assessment & Audit of Remote Platforms using TCG-SCAP synergies

Many enterprise solutions today are built upon complex distributed systems which are accessible to the users globally. Due to this global access, the security of the host platforms becomes critical. The platform administrators use security automation techniques such as those provided by Security Content Automation Protocol (SCAP) standards to protect the systems from the vulnerabilities that are reported daily; furthermore, they are responsible for keeping their systems compliant to the relevant security recommendations (governmental or industrial). Additionally, third party audit and certification processes are used to increase user trust in enterprise solutions. However, traditional audit and certification mechanisms are not continuous, that is, not frequent enough to deal with the daily reported vulnerabilities, and for that matter even auditors expect platform administrators to keep the systems updated. As a result, the end user is also forced to trust the platform administrators about the latest state of the platform. In this paper we develop an automated security audit and certification system (ASArP) which can be used by platform users or by third party auditors. We use security automation techniques for continuous monitoring of the platform security posture and make the results trustworthy by using trusted computing (TCG) techniques. The prototype development of ASArP validates the implementation feasibility; it also provides performance benchmarks which show that the ASArP based audit and certification can be done much more frequently (e.g. daily or weekly). The feasibility of ASArP based continuous audits is significantly better than traditional platform audits which are dependent on the physical presence of the auditors, thus making frequent audits much more expensive and operationally infeasible.

Social Networking Privacy: Understanding the Disconnect from Policy to Controls

A proposed method for mapping privacy policy statements to privacy controls can help providers improve data management transparency, thereby increasing user trust.