Explaining Automations in IoT Settings

Automation systems are increasingly present in our everyday environments, driven by the availability of a growing number of sensors and connected devices. However, users often struggle to understand or control these systems due to a lack of clear understanding of their behaviour, especially when multiple automations are active simultaneously. This paper explores effective strategies for delivering explanations in such settings by comparing two interaction modalities: visual and conversational. This paper presents an approach to addressing such issues and a mixed-method, within-subjects study involving 16 participants that has provided valuable insights for the design of more transparent and explainable automated systems.

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Publication Details

Journal
IEEE Access
Published
2025-01-01
DOI
https://doi.org/10.1109/access.2025.3609900
Primary Topic
Anomaly Detection Techniques and Applications
Type
article
Field-Weighted Citation Impact
0.00
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article

Explaining Automations in IoT Settings

Fabio Paternò, Andrea Mattioli, Simone Gallo, Sara Maenza
IEEE Access
Anomaly Detection Techniques and Applications
article

Explaining Automations in IoT Settings

Fabio Paternò, Andrea Mattioli, Simone Gallo, Sara Maenza
article en

Abstract

Automation systems are increasingly present in our everyday environments, driven by the availability of a growing number of sensors and connected devices. However, users often struggle to understand or control these systems due to a lack of clear understanding of their behaviour, especially when multiple automations are active simultaneously. This paper explores effective strategies for delivering explanations in such settings by comparing two interaction modalities: visual and conversational. This paper presents an approach to addressing such issues and a mixed-method, within-subjects study involving 16 participants that has provided valuable insights for the design of more transparent and explainable automated systems.

IEEE AccessVol. 13
Istituto di Scienza e Tecnologie dell'Informazione "Alessandro Faedo" (IT)
Openalex Percentile: Top 87%
Anomaly Detection Techniques and Applications
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