Towards an empirically developed scale for measuring trust

Gulati, Siddharth; Sousa, Sónia; Lamas, David

doi:10.1145/3335082.3335116

Cited by 4 publications

(2 citation statements)

References 10 publications

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“…For instance, self-reporting survey instruments, such as those developed in [10][11][12], that are predominantly used for measuring trust are subjective and not objective. Thus, it is not possible to use self-reporting instruments to assess trust in real-time because only after completing one or more tasks/interactions are such instruments applied [13,14].…”

Section: Introductionmentioning

confidence: 99%

Measuring Trust with Psychophysiological Signals: A Systematic Mapping Study of Approaches Used

Ajenaghughrure

Sousa

Lamas

2020

MTI

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Trust plays an essential role in all human relationships. However, measuring trust remains a challenge for researchers exploring psychophysiological signals. Therefore, this article aims to systematically map the approaches used in studies assessing trust with psychophysiological signals. In particular, we examine the numbers and frequency of combined psychophysiological signals, the primary outcomes of previous studies, and the types and most commonly used data analysis techniques for analyzing psychophysiological data to infer a trust state. For this purpose, we employ a systematic mapping review method, through which we analyze 51 carefully selected articles (studies focused on trust using psychophysiology). Two significant findings are as follows: (1) Psychophysiological signals from EEG(electroencephalogram) and ECG(electrocardiogram) for monitoring peripheral and central nervous systems are the most frequently used to measure trust, while audio and EOG(electro-oculography) psychophysiological signals are the least commonly used. Moreover, the maximum number of psychophysiological signals ever combined so far is three (2). Most of which are peripheral nervous system monitoring psychophysiological signals that are low in spatial resolution. (3) Regarding outcomes: there is only one tool proposed for assessing trust in an interpersonal context, excluding trust in a technology context. Moreover, there are no stable and accurate ensemble models that have been developed to assess trust; all prior attempts led to unstable but fairly accurate models or did not satisfy the conditions for combining several algorithms (ensemble). In conclusion, the extent to which trust can be assessed using psychophysiological measures during user interactions (real-time) remains unknown, as there several issues, such as the lack of a stable and accurate ensemble trust classifier model, among others, that require urgent research attention. Although this topic is relatively new, much work has been done. However, more remains to be done to provide clarity on this topic.

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Section: Introductionmentioning

confidence: 99%

Measuring Trust with Psychophysiological Signals: A Systematic Mapping Study of Approaches Used

Ajenaghughrure

Sousa

Lamas

2020

MTI

Self Cite

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“…Following [22,46,75], we asked participants for their level of agreement with each statement on a five-point Likert scale. The results (see Figure 4) are very positive overall, with only three, respectively four, respondents choosing the neutral answer for the questions of trust (Q3, Q4). )…”

Section: Rq 1: Was the Toolchain Trusted?mentioning

confidence: 99%

Trustworthy Transparency by Design

Zieglmeier¹,

Pretschner²

2021

Preprint

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Individuals lack oversight over systems that process their data. This can lead to discrimination and hidden biases that are hard to uncover. Recent data protection legislation tries to tackle these issues, but it is inadequate. It does not prevent data misusage while stifling sensible use cases for data.We think the conflict between data protection and increasingly data-based systems should be solved differently. When access to data is given, all usages should be made transparent to the data subjects. This enables their data sovereignty, allowing individuals to benefit from sensible data usage while addressing potentially harmful consequences of data misusage.We contribute to this with a technical concept and an empirical evaluation. First, we conceptualize a transparency framework for software design, incorporating research on user trust and experience. Second, we instantiate and empirically evaluate the framework in a focus group study over three months, centering on the user perspective.Our transparency framework enables developing software that incorporates transparency in its design. The evaluation shows that it satisfies usability and trustworthiness requirements. The provided transparency is experienced as beneficial and participants feel empowered by it. This shows that our framework enables Trustworthy Transparency by Design.

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