Third-party web APIs are becoming ever more popular as the API economy continues to grow. Software developers often integrate them into their own applications. The issue is that even if a developer thoroughly tests that their application works properly with the third-party web API, the owner of that API can completely change the code at any time, or take the API offline altogether, either temporarily or permanently. This makes for potentially less stable or reliable applications. This report attempts to determine what some of the factors are that most influence software developers’ trust in any given third party Web API. To do this, 42 individuals involved with software development were surveyed. Documentation and reliability came through as the strongest factors influencing their trust, but there is no general consensus on other factors. Further work could be done to confirm that these two factors are what influence developer trust the most, as well as work to determine which factor sought to influence developers’ trust in any given third-party web API, and thus work towards more reliable applications being developed as the API economy continues to grow.

Automated systems are integral in the development of modern aircraft, especially for complex military aircraft. Pilot Trust in Automation (TIA) in these systems is vital for optimizing the pilot-vehicle interface and ensuring pilots use the systems appropriately to complete required tasks. The objective of this research was to develop and validate a TIA scale and survey methodology to identify and mitigate trust deficiencies with automated systems for use in Army Aviation testing. There is currently no standard TIA assessment methodology for U.S. Army aviation pilots that identifies trust deficiencies and potential mitigations. A comprehensive literature review was conducted to identify prominent TIA factors present in similar studies. The compiled list of factors and associated definitions were used in a validation study that utilized the Analytic Hierarchy Process (AHP) as a pair-wise comparison tool to identify TIA factors most relevant to Army pilots. A notional survey, the Aviation Systems – Trust Survey (AS-TS), was developed from the identified factors and pilots were used as subjects in scenario-based testing to establish construct validity for the survey. Exploratory factor analysis was conducted after data collection and a validated survey was produced. A follow-on study interviewed Army test and evaluation experts to refine the survey methodology and ensure appropriate context for the recommended mitigations. A final packet was developed that included instructions for the rating scale, associated item definitions, and recommended mitigations for trust deficiencies. Future research will focus on other Army demographics to determine the generalizability of the AS-TS.

BAKGRUND: Den automatiserade ekonomiska rådgivningen i form av robotrådgivning är ett nytt fenomen som funnits på den svenska marknaden i knappt 15 år. Den tidigare populära traditionella rådgivningen har i takt med robotrådgivningens framfart fått ett konkurrenskraftigt alternativ. Förtroende anses vara en av de viktigaste aspekterna för individen när det kommer till valet av ekonomisk rådgivare, men trots att flera studier gjorts inom förtroendeområdet vid ekonomisk rådgivning saknas det fortfarande kunskap om vilka faktorer som förklarar förtroendet till robotrådgivning. Således finns incitament för att täcka den kunskapslucka som idag finns gällande individer som ej aktivt använder sig av robotrådgivare.    SYFTE: Syftet med studien är att skapa förståelse kring vilka faktorer som förklarar svenska individers förtroende till robotrådgivning utifrån ett icke-användande perspektiv, samt hur dessa faktorer främjar eller hämmar förtroendet till tjänsten.   GENOMFÖRANDE: Studien har genomförts med en kvalitativ metod genom semi-strukturerade intervjuer med individer som ej aktivt använder robotrådgivning men som ändå besitter kunskap om hur tjänsten fungerar. Respondenterna fick genom intervjuerna besvara fördjupande frågor om specifika förtroendefaktorer samt hur de uppfattar förtroendet till robotrådgivning.   SLUTSATS: Studiens resultat utifrån den insamlade empirin visade att respondenterna har ett högt förtroende till robotrådgivning där kompetens och följdriktighet visade sig vara de främst främjande faktorerna till förtroendet. Kommunikation var delvis främjande, delvis hämmande, medan medkänslan i och med sin avsaknad vid robot-rådgivning framhävdes som en svagt främjande faktor. Anseende, bemötande, kostnad och transparens lyftes också som förklarande förtroendefaktorer till robotrådgivning. / BACKGROUND: The automated financial advisor in the form of robo-advisors is a new phenomenon that has existed in the Swedish market for almost 15 years. The already popular traditional financial advisory has, in step with the progress of robo-advisory, been given a competitive alternative. Trust is considered as one of the most important aspects for the individual when it comes to choosing financial advisors, but despite several studies done in the area of ​​trust in financial advisory, there is still a lack of knowledge about which factors are explaining trust in robo-advisory. Thus, there are incentives to cover the knowledge gap that currently exists for individuals who do not actively use robo-advisory.   AIM: The aim with this thesis is to gain knowledge about which factors are explaining individuals’ trust in robo-advisors from a non-user perspective, as well as to investigate in what way these factors might enable or disable the trust in the service.    COMPLETION: To fulfill the aim of this study, a qualitative method has been implemented through semi-structured interviews with individuals that do not actively use a robo-advisor. Through the interviews, the respondents were asked to answer in-depth questions about specific trust factors and how they perceive the trust in robo-advisory.   CONCLUSION: The overall results of the study based on the collected empirical data showed that the respondents have a high level of trust in robo-advisory, where competence and consistency proved to be the main enabling factors for trust. Communication was partly enabling, partly disabling, while compassion, due to its lack within robo-advisory, was emphasized as a vague promoting factor. Reputation, treatment, cost and transparency were also highlighted as explanatory factors of trust in robo-advisory.

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This thesis explores a methodology to quantify trust in wearable medical devices (WMD) by addressing two main challenges: identifying key factors influencing trust and developing a formal framework for precise trust quantification under uncertainty. The work empirically validates trust factors and uses a Bayesian network to quantify trust. The thesis further employs a data-driven approach to estimate Bayesian parameters, facilitating query-based inference and validating the trust model with real and synthetic datasets, culminating in a customizable parameterized trust evaluation prototype for WMD. / Advances in sensor and digital communication technologies have revolutionized the capabilities of wearable medical device (WMD) to monitor patients’ health remotely, raising growing concerns about trust in these devices. There is a need to quantify trust in WMD for their continued acceptance and adoption by different users. Quantifying trust in WMD poses two significant challenges due to their subjective and stochastic nature. The first challenge is identifying the factors that influence trust in WMD, and the second is developing a formal framework for precise quantification of trust while taking into account the uncertainty and variability of trust factors. This thesis proposes a methodology to quantify trust in WMD, addressing these challenges. In this thesis, first, we devise a method to empirically validate dominant factors that influence the trustworthiness of WMD from the perspective of device users. We identified the users’ awareness of trust factors reported in the literature and additional user concerns influencing their trust. These factors are stepping stones for defining the specifications and quantification of trust in WMD. Second, we develop a probabilistic graph using Bayesian network to quantify trust in WMD. Using the Bayesian network, the stochastic nature of trust is viewed in terms of probabilities as subjective degrees of belief by a set of random variables in the domain. We define each random variable in the network by the trust factors that are identified from the literature and validated by our empirical study. We construct the trust structure as an acyclic-directed graph to represent the relationship between the variables compactly and transparently. We set the inter-node relationships, using the goal refinement technique, by refining a high-level goal of trustworthiness to lower-level goals that can be objectively implemented as measurable factors. Third, to learn and estimate the parameters of the Bayesian network, we need access to the probabilities of all nodes, as assuming a uniform Gaussian distribution or using values based on expert opinions may not fully represent the complexities of the factors influencing trust. We propose a data-driven approach to generate priors and estimate Bayesian parameters, in which we use data collected from WMD for all the measurable factors (nodes) to generate priors. We use non-functional requirement engineering techniques to quantify the impacts between the node relationships in the Bayesian network. We design propagation rules to aggregate the quantified relationships within the nodes of the network. This approach facilitates the computation of conditional probability distributions and enables query-based inference on any node, including the high-level trust node, based on the given evidence. The results of this thesis are evaluated through several experimental validations. The factors influencing trust in WMD are empirically validated by an extensive survey of 187 potential users. The learnability, and generalizability of the proposed trust network are validated with a real dataset collected from three users of WMD in two conditions, performing predefined activities and performing regular daily activities. To extend the variability of conditions, we generated an extensive and representative synthetic dataset and validated the trust network accordingly. Finally, to test the practicality of our approach, we implemented a user-configurable, parameterized prototype that allows users of WMD to construct a customizable trust network and effectively compare the trustworthiness of different devices. The prototype enables the healthcare industry to adapt and adopt this method to evaluate the trustworthiness of WMD for their own specific use cases. / Thesis / Doctor of Philosophy (PhD) / In this thesis, two challenges in quantifying trust in wearable medical devices, are addressed. The first challenge is the identification of factors influencing trust which are inherently subjective and vary widely among users. To address this challenge, we conducted an extensive survey to identify and validate the trust factors. These factors are stepping stones for defining the specifications and quantifying trust in wearable medical devices. The second challenge is to develop a precise method for quantification of trust while taking into account the uncertainty and variability of trust factors. We constructed a Bayesian network, that captures the complexities of trust as probabilities of the trust factors (identified from the survey) and developed a data-driven approach to estimate the parameters of the Bayesian network to compute the measure of trust. The findings of this thesis are empirically and experimentally validated across multiple use cases, incorporating real and synthetic data, various testing conditions, and diverse Bayesian network configurations. Additionally, we developed a customizable, parameterized prototype that empowers users and healthcare providers to effectively assess and compare the trustworthiness of different wearable medical devices.