Modeling Trust in COVID-19 Contact-Tracing Apps Using the Human-Computer Trust Scale: Online Survey Study
Notice bibliographique
Résumé
BACKGROUND: The COVID-19 pandemic has caused changes in technology use worldwide, both socially and economically. This pandemic crisis has brought additional measures such as contact-tracing apps (CTAs) to help fight against spread of the virus. Unfortunately, the low adoption rate of these apps affected their success. There could be many reasons for the low adoption, including concerns of security and privacy, along with reported issues of trust in CTAs. Some concerns are related with how CTAs could be used as surveillance tools or their potential threats to privacy as they involve health data. For example, in Estonia, the CTA named HOIA had approximately 250,000 downloads in the middle of January 2021. However, in 2021, only 4.7% of the population used HOIA as a COVID-19 CTA. The reasons for the low adoption include lack of competency, and privacy and security concerns. This lower adoption and the lack of trustworthiness persist despite efforts of the European Union in building ethics and trustworthy artificial intelligence (AI)-based apps. OBJECTIVE: The aim of this study was to understand how to measure trust in health technologies. Specifically, we assessed the usefulness of the Human-Computer Trust Scale (HCTS) to measure Estonians' trust in the HOIA app and the causes for this lack of trust. METHODS: The main research question was: Can the HCTS be used to assess citizens' perception of trust in health technologies? We established four hypotheses that were tested with a survey. We used a convenience sample for data collection, including sharing the questionnaire on social network sites and using the snowball method to reach all potential HOIA users in the Estonian population. RESULTS: Among the 78 respondents, 61 had downloaded the HOIA app with data on usage patterns. However, 20 of those who downloaded the app admitted that it was never opened despite most claiming to regularly use mobile apps. The main reasons included not understanding how it works, and privacy and security concerns. Significant correlations were found between participants' trust in CTAs in general and their perceived trust in the HOIA app regarding three attributes: competency (P<.001), risk perception (P<.001), and reciprocity (P=.01). CONCLUSIONS: This study shows that trust in the HOIA app among Estonian residents did affect their predisposition to use the app. Participants did not generally believe that HOIA could help to control the spread of the virus. The result of this work is limited to HOIA and health apps that use similar contact-tracing methods. However, the findings can contribute to gaining a broader understanding and awareness of the need for designing trustworthy technologies. Moreover, this work can help to provide design recommendations that ensure trustworthiness in CTAs, and the ability of AI to use highly sensitive data and serve society.
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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,002 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,001 | 0,002 |
| Études des sciences et des technologies | 0,002 | 0,000 |
| Communication savante | 0,001 | 0,002 |
| Science ouverte | 0,003 | 0,002 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».