The Future of Privacy Policies: A Privacy Nutrition Label Filled With Fair Information Practices
Notice bibliographique
Résumé
E-commerce continues to blossom as evidenced by online retail sales in excess of $33 billion over the first quarter 2008. This growth helps spur the staggering economy but also magnifies the serious threats surrounding personally identifying information (PII) submitted during e-commerce transactions. The most common threats, such as identity theft and aggregated data files, do the most damage when companies are careless (i.e., losing laptops filled with unencrypted data) or callous (selling data on the open market) with the PII they collect. The first line of defense against these threats is the electronic privacy policy. In theory, privacy policies are supposed to force companies to analyze and strengthen their privacy practices and then provide Web surfers with a detailed picture of what happens to their information upon submission. Privacy policies are most effective when Web site visitors can locate, read and comprehend their terms. Armed with this knowledge, individuals are supposed to make accurate privacy assessments before submitting information online. Problematically, contemporary privacy policies fail to live up to their promise because they are posted inconspicuously, purposefully vague and filled with legalese. This inaccessibility leads Web surfers to ignore privacy practices completely while they continue to submit PII blindly.Privacy policies can be effective if companies clearly and conspicuously discuss how their privacy terms relate to fair information practices (FIPs). FIPs are widely agreed upon guidelines covering the most important areas of the data trade - PII collection, use, storage and dissemination. The Federal Trade Commission has designated the five core FIPs to be notice, choice, access, integrity and enforcement. This article argues that a standardized privacy nutrition label - similar to the labels required by the Nutrition Labeling and Education Act - posted conspicuously on all e-commerce homepages can increase policy effectiveness. These federally mandated labels require companies to discuss their privacy practices in relation to each Key FIP. Although companies need not adopt specific policy terms or run their practices through a governmental clearinghouse, they must honestly disclose their practices. This is true of even the most unpopular practices such as external PII dissemination. Over time, consumers will become aware of these standardized labels, begin to understand FIPs, differentiate between privacy-protective and privacy-invasive practices and make better decisions before submitting PII.
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 | 0,001 | 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 | 0,003 |
| 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 ».