Recognising Cyber Blockades as Crimes Against Humanity: Can International Criminal Law Keep Up?
Bibliographic record
Abstract
As a result of the heavily digitalised world on top of our increasing online presence and interconnectedness, states and civilian populations are becoming more and more vulnerable to cyber attacks. It is thus imperative to examine the dangers large scale cyber attacks pose with respect to their contribution to potential human suffering. As such, these large scale cyber attacks, especially a cyber blockade, may be able to constitute an international crime. The Prosecutor of the International Criminal Court announced at the Digital Front Lines conference that his office is willing to investigate cyber operations as potential war crimes given that they are capable of causing severe consequences akin to kinetic warfare. (Yoon Onn, 2023) This is the first significant step towards recognising the harmful effects of malicious cyber operations as international crimes. However, not only is the Rome Statute itself silent on cyber operations as potential international crimes, the ICC has not yet seen a case concerning malicious cyber activities as either a war crime or as a crime against humanity. As such, the central question the paper seeks to answer is whether the Rome Statute could potentially encompass cyber blockades as the crime against humanity of “other inhumane acts” under Article 7(1)(k) of the Rome Statute. The paper looks at crimes against humanity for three reasons: firstly, Karim Khan KC has already touched on cyber attacks potentially prosecuted as war crimes, as mentioned above, thus the knowledge gap is gradually being bridged with respect to war crimes. Secondly, there is an absence of any regulatory framework should a cyber blockade be unleashed in peacetime, where international humanitarian principles do not apply. Thirdly, establishing a cyber blockade as a crime against humanity would lead to greater individual criminal responsibility as opposed to a war crimes conviction. This, in turn, would send a strong deterrent message in both war and peace.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.012 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.009 | 0.048 |
| Scholarly communication | 0.027 | 0.045 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.024 | 0.027 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".