Bør civile data anses for beskyttede "objekter" under den humanitære folkeret?
Notice bibliographique
Résumé
International Humanitarian Law (IHL) aims to limit the effects of armed conflict. It divides the world into legitimate military objectives that may be lawfully targeted during armed conflict, and protected civil ian persons and objects that are to be protected from attack. IHL provides clear guidance on how to distinguish one category from the other – at least in the physical realm. The relatively new domain of cyberspace, however, is characterized by a number of features unknown at the time of IHL’s foundation. As a consequence, there is currently no consensus on whether and how the rules on targeting apply to digital data. However, recent examples from the war in Ukraine have shown that cyberspace and attacks on data are part of modern armed conflicts, and the question of the legal status of data in IHL is therefore highly topical. On that basis, this report investigates whether data should be understood as protected civilian objects through an analysis of the legal debate on the matter and statements made by states in this regard. The report identifies two major positions in the legal debate on the status of data: The traditionalist school, which emphasizes the fact that ‘an object’ has traditionally been defined as something visible and tangible, and that data therefore should not be considered an object, as the protection provided by IHL would otherwise extend beyond its intended scope; and the progressive school, which emphasizes that the purpose of IHL is to protect civilians from the effects of armed conflict, and that civilian data should therefore be considered an object, as the protections provided by IHL would otherwise be more restricted than intended. Both schools posit valid legal interpretations but arrive at polar opposite conclusions. This report therefore suggests a contextual interpretation as a supplement to the two schools: As the rules and legal structure of relevant IHL regulation intend to divide the world into objects that are either legitimate military objectives or protected civilian objects, it cannot have been the intention for anything directly targetable in an armed conflict to fall outside the scope of the rules. Through this approach, data should be considered an object and civilian data protected civilian objects. As there is not yet a clear picture of which interpretation states generally support, the report goes on to review government statements and other written materials from states that have expressed opinions on the matter and compare them with official Danish statements on the ques tion. The report finds Denmark to be one of only three states maintaining a traditionalist interpretation of the question, while significantly more states – including several of Denmark’s close allies – support the progressive approach. Largest, however, is the group of states that either leave the question open or have yet to comment on it. Based on the analysis of the legal debate and state positions, the report concludes that the interests of Denmark are best served through the adoption of an open position, where technological advancements can give rise to new considerations on the matter, and through contributing to a clarification of the legal concept of ‘military operations’, which might shed light on aspects of the protection of civilian data under IHL, regardless of the outcome of the current debate on data as an object.
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 | 0,016 | 0,037 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,003 | 0,004 |
| Études des sciences et des technologies | 0,008 | 0,025 |
| Communication savante | 0,033 | 0,033 |
| Science ouverte | 0,002 | 0,010 |
| Intégrité de la recherche | 0,008 | 0,008 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,028 | 0,013 |
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 ».