Can PIPEDA ‘Face’ the Challenge? An Analysis of the Adequacy of Canada’s Private Sector Privacy Legislation against Facial Recognition Technology
Notice bibliographique
Résumé
Facial recognition technology is one of the most intrusive and privacy threatening technologies available today. The literature around this technology mainly focuses on its use by the public sector as a mass surveillance tool; however, the private sector uses of facial recognition technologies also raise significant privacy concerns. This paper aims to identify and examine the privacy implications of the private sector uses of facial recognition technologies and the adequacy of Canada’s federal private sector privacy legislation, the Personal Information Protection and Electronic Documents Act (PIPEDA), in addressing these privacy concerns. Facial templates produced and recorded by these technologies are types of biometric information. While all biometric information are highly sensitive in nature and require stricter regulatory protections, this paper argues that facial recognition data poses a more significant risk to individuals’ privacy and autonomy than other forms of biometric data. First, facial recognition technology can be used on a person from large distances and completely surreptitiously. Second, hiding one’s face or avoiding the technology in daily life is hard to accomplish, if not impossible. Third, there is already a vast database of personally identified facial images available in the hands of private organizations, such as social media companies, containing the sensitive data of millions of people ready to be identified. This paper takes the position that PIPEDA does not provide adequate protection of individuals’ privacy against facial recognition technologies and significant amendments to the Act are urgently required. Sensitive personal information, which includes facial recognition data, should be defined as a special category of personal information along with stricter protections on their collection, use, retention and disclosure. The role of consent should be re-defined concerning highly sensitive facial recognition data, clear enforcement powers should be given to the Privacy Commissioner of Canada in enforcing the Act and mechanisms to ensure compliance should be regulated in the forms of substantial fines for violations and a private right of action for citizens. However, the paper argues that due to PIPEDA’s limited scope of application and constitutional challenges, amending the Act would not be enough to protect all Canadians from the risks this technology poses. Accordingly, the paper concludes that along with the proposed amendments to PIPEDA, provincial legislations should also be enacted and enforced to ensure the protection of Canadians’ privacy and autonomy against the threats posed by facial recognition technology.
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 | 0,000 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,001 | 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 ».