Facebook and the Cambridge Analytica Scandal: Privacy and Personal Data Protections in Canada
Notice bibliographique
Résumé
In 2018, the Cambridge Analytica/Facebook scandal made front page news, a data breach that allowed a third-party -Cambridge Analytica -access to the personal data of millions in several countries, including over 600,000 Canadians.The scandal brought to light privacy issues to regulator and in the aftermath, Canada conducted an investigation into this unsanctioned use of data.This thesis explores the details of that scandal and the resulting Canadian investigation by the Standing Committee on Access to Information, Privacy and Ethics (ETHI) and the Office of the Privacy Commissioner (OPC), as well as drawing on information from the 2009 Canadian Internet Policy and Public Interest Clinic (CIPPIC) complaint with the OPC, and the Broadcasting and Telecommunications Legislative Review (BTLR).These public records are used to provide a lens through which to explore topics of privacy and personal data protection in Canada and what they might mean in a social media platform context.This thesis explores the different regulatory mechanisms and makes some recommendations to improve personal data protection and privacy regulations in Canada, including behavioral and structural regulatory solutions that might mitigate similar such scandals in the future.Facebook users, including over 600,000 people in Canada (ETHI, 2018, p. 9).This scandal ignited a flurry of activity by governments around questions of data protection and privacy, resulting in numerous public inquiries to assess how this unsanctioned use of data occurred and to mitigate this from occurring again (Winseck and Puppis, 2019).Many of the inquiries touched on a wide range of issues about social media, not only focusing on the Cambridge Analytica scandal, which resulted in broader investigations about social media in general.The United Kingdom, Canadian parliamentary inquiries, and the International Grand Committee led by the United Kingdom with representatives from 14 countries, however, focused solely on the scandal.These three inquiries provide a very detailed record of what happened, and in this thesis, I refer to these to inform my analysis.Prior to this scandal, governments had expressed concerns about privacy and data protection on social media platforms, 2 but the Cambridge Analytica scandal marked a tipping point in terms of social media regulation.The scandal exemplifies how the very foundation of social media platforms, the data that they collect, aggregate, and employ to provide tailored services to users and options for marketing to advertisers, can be exploited for less than positive and ethical goals (ETHI, 2018, p. vii).This is especially the case with respect to a platform as widely used and well-known as Facebook, which usual platform business model that ended in 2015, where third-party applications were permitted to collect and retain user information.Cambridge Analytica however reused data collected for one purpose in unanticipated ways, beyond what would be the "reasonable expected use" by a third-party application.2 Some examples of growing government attention to social media regulation can be found in the 2009 CIPPIC Complaint with the Office of the
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 machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,006 | 0,017 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,001 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,002 | 0,005 |
| Études des sciences et des technologies | 0,050 | 0,019 |
| Communication savante | 0,020 | 0,003 |
| Science ouverte | 0,002 | 0,005 |
| Intégrité de la recherche | 0,004 | 0,007 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,009 | 0,001 |
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 source (Gemma direct ou Codex distillé), 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 ».