The Value of Measuring Trust in AI - A Socio-Technical System Perspective

Benk, Michaela; Tolmeijer, Suzanne; Wangenheim, Florian von; Ferrario, Andrea

doi:10.48550/arxiv.2204.13480

Cited by 2 publications

(1 citation statement)

References 54 publications

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“…VSD has recently been applied in digital health contexts, such as in the development of a digital assistant for physiotherapeutic treatments used at home [54] and the design of a preventive health check app [55]. In the human-computer interaction domain, researchers have suggested VSD as a framework to guide the understanding of AI as a sociotechnical system and the measurement of trust in human-AI interactions [56].…”

Section: Value Sensitive Designmentioning

confidence: 99%

Investigating Employees’ Concerns and Wishes Regarding Digital Stress Management Interventions With Value Sensitive Design: Mixed Methods Study

Kerr¹,

Naegelin²,

Benk³

et al. 2023

J Med Internet Res

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Background Work stress places a heavy economic and disease burden on society. Recent technological advances include digital health interventions for helping employees prevent and manage their stress at work effectively. Although such digital solutions come with an array of ethical risks, especially if they involve biomedical big data, the incorporation of employees’ values in their design and deployment has been widely overlooked. Objective To bridge this gap, we used the value sensitive design (VSD) framework to identify relevant values concerning a digital stress management intervention (dSMI) at the workplace, assess how users comprehend these values, and derive specific requirements for an ethics-informed design of dSMIs. VSD is a theoretically grounded framework that front-loads ethics by accounting for values throughout the design process of a technology. Methods We conducted a literature search to identify relevant values of dSMIs at the workplace. To understand how potential users comprehend these values and derive design requirements, we conducted a web-based study that contained closed and open questions with employees of a Swiss company, allowing both quantitative and qualitative analyses. Results The values health and well-being, privacy, autonomy, accountability, and identity were identified through our literature search. Statistical analysis of 170 responses from the web-based study revealed that the intention to use and perceived usefulness of a dSMI were moderate to high. Employees’ moderate to high health and well-being concerns included worries that a dSMI would not be effective or would even amplify their stress levels. Privacy concerns were also rated on the higher end of the score range, whereas concerns regarding autonomy, accountability, and identity were rated lower. Moreover, a personalized dSMI with a monitoring system involving a machine learning-based analysis of data led to significantly higher privacy (P=.009) and accountability concerns (P=.04) than a dSMI without a monitoring system. In addition, integrability, user-friendliness, and digital independence emerged as novel values from the qualitative analysis of 85 text responses. Conclusions Although most surveyed employees were willing to use a dSMI at the workplace, there were considerable health and well-being concerns with regard to effectiveness and problem perpetuation. For a minority of employees who value digital independence, a nondigital offer might be more suitable. In terms of the type of dSMI, privacy and accountability concerns must be particularly well addressed if a machine learning-based monitoring component is included. To help mitigate these concerns, we propose specific requirements to support the VSD of a dSMI at the workplace. The results of this work and our research protocol will inform future research on VSD-based interventions and further advance the integration of ethics in digital health.

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Section: Value Sensitive Designmentioning

confidence: 99%

Investigating Employees’ Concerns and Wishes Regarding Digital Stress Management Interventions With Value Sensitive Design: Mixed Methods Study

Kerr¹,

Naegelin²,

Benk³

et al. 2023

J Med Internet Res

Self Cite

View full text Add to dashboard Cite

show abstract

Investigating Employees’ Concerns and Wishes Regarding Digital Stress Management Interventions With Value Sensitive Design: Mixed Methods Study (Preprint)

Kerr¹,

Naegelin²,

Benk³

et al. 2022

Preprint

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BACKGROUND Work-related stress places a large economic and disease burden on society. Recent technological advances include digital health interventions to help employees prevent and manage stress at work effectively. Although such digital solutions come with an array of ethical risks, especially if they involve biomedical Big Data, the incorporation of employees’ values in their design and deployment has been widely overlooked. OBJECTIVE To bridge this gap, we used the Value Sensitive Design framework to 1) identify relevant values concerning a digital stress management intervention (dSMI) at the workplace, to 2) assess how users apprehend these values, and to 3) derive specific requirements for an ethics-informed design of dSMIs. METHODS We conducted a literature search to identify the relevant values of dSMIs at the workplace. To understand how potential users apprehend these values and derive design requirements, we ran an online study with employees of a Swiss company that contained closed and open questions, allowing both quantitative and qualitative analyses. RESULTS The values health and well-being, privacy, autonomy, accountability and identity were identified from our literature search. The statistical analysis of 170 responses from the online study revealed that intention to use and perceived usefulness of a dSMI were moderate to high. Employees’ moderate to high health and well-being concerns included worries that a dSMI would not be effective or even amplify their stress levels. Privacy concerns were also rated on the higher end of the score range, while concerns regarding autonomy, accountability and identity were rated lower. Moreover, a personalised dSMI that included a monitoring system involving a machine learning-based analysis of data led to significantly higher privacy (P=.009) and accountability concerns (P=.04) compared to one without. In addition, integrability, user friendliness and digital independence emerged as novel values from the qualitative analysis of 85 text responses. CONCLUSIONS While a majority of surveyed employees is willing to use a dSMI at the workplace, there are considerable health and well-being concerns with regards to effectiveness and problem perpetuation. For a minority of employees, who value digital independence, a non-digital offer might even be more suitable. In terms of the type of dSMI, privacy and accountability concerns have to be particularly well-addressed if a machine learning-based monitoring component is included. To help mitigate these concerns, we propose specific requirements to support the value-sensitive design of a dSMI at the workplace. The results of this work and our research protocol will inform future research on VSD-based interventions and further advance the integration of ethics in digital health.

show abstract

The Value of Measuring Trust in AI - A Socio-Technical System Perspective

Cited by 2 publications

References 54 publications

Investigating Employees’ Concerns and Wishes Regarding Digital Stress Management Interventions With Value Sensitive Design: Mixed Methods Study

Investigating Employees’ Concerns and Wishes Regarding Digital Stress Management Interventions With Value Sensitive Design: Mixed Methods Study

Investigating Employees’ Concerns and Wishes Regarding Digital Stress Management Interventions With Value Sensitive Design: Mixed Methods Study (Preprint)

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