Trust in activity tracker measurement and its link to user acceptance

Trommler, Daniel; Attig, Christiane; Franke, Thomas

doi:10.18420/muc2018-mci-0361

Cited by 2 publications

(2 citation statements)

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“…In five similar observation blocks with 10 stimuli each, users had to observe and draw conclusions about the underlying algorithm. After each block SIPA and trust (using the FOST Scale [38]) were measured. The experiment for S2 was constructed similarly.…”

Section: Empirical Applications Of Sipa In Human-ai Cooperationmentioning

confidence: 99%

“…Users saw four similar blocks of 15 stimuli each, and were asked to observe and draw conclusions about the underlying algorithm. After each block SIPA and trust (using the FOST Scale [38]) were measured. Finally, the automated insulin delivery simulation used to create scenarios for S2 was transformed into an interactive simulator for S3.…”

Section: Empirical Applications Of Sipa In Human-ai Cooperationmentioning

confidence: 99%

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Perceive, Understand & Predict - Empirical Indication for Facets in Subjective Information Processing Awareness

Schrills¹,

Kargl²,

Bickel³

et al. 2022

Preprint

Self Cite

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When quantitatively measuring how humans experience the cooperation with intelligent systems, validated instruments grounded in established fields such as human-automation interaction research are needed. We derived Subjective Information Processing Awareness (SIPA) from core conceptions of Situation Awareness. SIPA describes to what extent interacting with an intelligent system enables users to experience 1) Transparency 2) Understandability and 3) Predictability of the system’s information processing. The SIPA concept was operationalized as a 6-item scale. The goal of the present research was to examine the psychometric characteristics of the SIPA scale as well as the internal structure based on three samples from independent experiments with N = 162 participants. The SIPA scale showed possible applicability both as (a) a three-facet scale (three highly correlated facets) and (b) a one-dimensional scale as well as high reliability. Construct validity with related constructs such as explanation satisfaction or trust was expectedly high. Based on the results, the SIPA scale appears to be a promising tool for research on cooperative AI systems. We discuss what role SIPA and different effects of explanations on experienced traceability may have for future studies of, e.g., Human-AI teams.

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Section: Empirical Applications Of Sipa In Human-ai Cooperationmentioning

confidence: 99%

Section: Empirical Applications Of Sipa In Human-ai Cooperationmentioning

confidence: 99%

Perceive, Understand & Predict - Empirical Indication for Facets in Subjective Information Processing Awareness

Schrills¹,

Kargl²,

Bickel³

et al. 2022

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

How Do Users Experience Traceability of AI Systems? Examining Subjective Information Processing Awareness in Automated Insulin Delivery (AID) Systems

Schrills

Franke

2023

ACM Trans. Interact. Intell. Syst.

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When interacting with artificial intelligence (AI) in the medical domain, users frequently face automated information processing, which can remain opaque to them. For example, users with diabetes may interact daily with automated insulin delivery (AID). However, effective AID therapy requires traceability of automated decisions for diverse users. Grounded in research on human-automation interaction, we study Subjective Information Processing Awareness (SIPA) as a key construct to research users’ experience of explainable AI. The objective of the present research was to examine how users experience differing levels of traceability of an AI algorithm. We developed a basic AID simulation to create realistic scenarios for an experiment with N = 80, where we examined the effect of three levels of information disclosure on SIPA and performance. Attributes serving as the basis for insulin needs calculation were shown to users, who predicted the AID system’s calculation after over 60 observations. Results showed a difference in SIPA after repeated observations, associated with a general decline of SIPA ratings over time. Supporting scale validity, SIPA was strongly correlated with trust and satisfaction with explanations. The present research indicates that the effect of different levels of information disclosure may need several repetitions before it manifests. Additionally, high levels of information disclosure may lead to a miscalibration between SIPA and performance in predicting the system’s results. The results indicate that for a responsible design of XAI, system designers could utilize prediction tasks in order to calibrate experienced traceability.

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Trust in activity tracker measurement and its link to user acceptance

Cited by 2 publications

References 0 publications

Perceive, Understand & Predict - Empirical Indication for Facets in Subjective Information Processing Awareness

Perceive, Understand & Predict - Empirical Indication for Facets in Subjective Information Processing Awareness

How Do Users Experience Traceability of AI Systems? Examining Subjective Information Processing Awareness in Automated Insulin Delivery (AID) Systems

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Trust in activity tracker measurement and its link to user acceptance

Cited by 2 publications

References 0 publications

Perceive, Understand &amp; Predict - Empirical Indication for Facets in Subjective Information Processing Awareness

Perceive, Understand &amp; Predict - Empirical Indication for Facets in Subjective Information Processing Awareness

How Do Users Experience Traceability of AI Systems? Examining Subjective Information Processing Awareness in Automated Insulin Delivery (AID) Systems

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Product

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Perceive, Understand & Predict - Empirical Indication for Facets in Subjective Information Processing Awareness

Perceive, Understand & Predict - Empirical Indication for Facets in Subjective Information Processing Awareness