Personalization, analytics, and sponsored services: The challenges of applying PIPEDA to online tracking and profiling activities
Notice bibliographique
Résumé
In 2008, the online advertising industry was found to be worth 27 billion dollars, a figure that was projected to double over the subsequent four years.1 The reason for this extraordinary market growth can be explained by two factors. To begin with, current technology now makes it possible to gather a great variety of information associated with a particular device or individual, including browsing history, which can be used to create a profile specific to that device or individual. This practice facilitates more personalized advertising, tailored to the interests and tastes of the consumer. Secondly, many online services, in the form of information or entertainment, are offered for free to consumers as long as they accept the presence of advertising and the eventuality that their online behaviour will be tracked to a certain degree. Internet business models are increasingly being based on the notion of greater customization of services and products. This entails that there are huge amounts of data that need to be collected about online users. Moreover, online profiles present new types of concerns. For instance, although isolated pieces of profile information may not be sensitive, their context, especially in light of profiling or behavioural analysis practices, may become extremely sensitive. With the convergence between different technologies and the growing demand for applications that include location tracking capabilities, privacy concerns pertaining to tracking and profiling activities need to be properly addressed. Many authors have already outlined that there is definitely an issue with the fact that online users may not always be aware that the online profiling and tracking activities are happening in the first place (even if the website privacy policy is open about its practices, many studies have shown that consumers don’t read privacy policies). While this (lack of) consent issue is a serious one, this analysis will instead be focused on other issues: firstly, whether profile data is covered under data protection laws; secondly, whether tracking and profiling activities are legal in accordance with data protection laws such as the Personal Information Protection and Electronic Documents Act (PIPEDA)3; and finally, issues pertaining to the management of profile data, more specifically as they relate to granting access to profile data to individuals and what constitutes a reasonable retention period of the profile data.
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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 | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,001 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| 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 ».