Mapping the Apps: Ethical and Legal Issues with Crowdsourced Smartphone Data using mHealth Applications
Notice bibliographique
Résumé
More than 5 billion people in the world own a smartphone. More than half of these have been used to collect and process health-related data. As such, the existing volume of potentially exploitable health data is unprecedentedly large and growing rapidly. Mobile health applications (apps) on smartphones are some of the worst offenders and are increasingly being used for gathering and exchanging significant amounts of personal health data from the public. This data is often utilized for health research purposes and for algorithm training. While there are advantages to utilizing this data for expanding health knowledge, there are associated risks for the users of these apps, such as privacy concerns and the protection of their data. Consequently, gaining a deeper comprehension of how apps collect and crowdsource data is crucial. To explore how apps are crowdsourcing data and to identify potential ethical, legal, and social issues (ELSI), we conducted an examination of the Apple App Store and the Google Play Store in North America and Europe to identify apps that could potentially gather health data through crowdsourcing. Subsequently, we analyzed their privacy policies, terms of use, and other related documentation to gain insights into the utilization of users' data and the possibility of repurposing it for research or algorithm training purposes. More specifically, we reviewed privacy policies to identify clauses pertaining to the following key categories: research, data sharing, privacy/confidentiality, commercialization, and return of findings. Based on the results of these app search, we developed an App Atlas that presents apps which crowdsource data for research or algorithm training. We identified 46 apps available in the European and Canadian markets that either openly crowdsource health data for research or algorithm training or retain the legal or technical capability to do so. This app search showed an overall lack of consistency and transparency in privacy policies that poses challenges to user comprehensibility, trust, and informed consent. A significant proportion of applications presented contradictions or exhibited considerable ambiguity. For instance, the vast majority of privacy policies in the App Atlas contain ambiguous or contradictory language regarding the sharing of users' data with third parties. This raises a number of ethico-legal concerns which will require further academic and policy attention to ensure a balance between protecting individual interests and maximizing the scientific utility of crowdsourced data. This article represents a key first step in better understanding these concerns and bringing attention to this important issue. Supplementary Information: The online version contains supplementary material available at 10.1007/s41649-024-00296-3.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
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 | 0,006 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,002 | 0,001 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,001 | 0,005 |
| 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 ».