The humanitarian passive, press language, and the AI-mediated grammar of responsibility loss in Gaza
*TL;DR
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Modern conflict reporting does not always erase suffering. Often, it does something more subtle: it shows suffering while weakening the grammar of responsibility.
In AI-generated summaries, automated headlines, platform moderation outputs, and press rewrites, Palestinian civilians can appear as “killed,” “displaced,” “affected,” or “caught in conflict,” while the actor responsible for producing that condition disappears from the sentence.
*That structure is the humanitarian passive.
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It is not simply passive voice. It is a political grammar through which suffering remains visible while agency becomes optional.
This post builds on two academic papers:
Suffering Without Perpetrators: The Humanitarian Passive in AI-Generated Conflict Discourse
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=6753123
The Grammar of Asymmetric Visibility: AI, Zionism, and the Reallocation of Political Agency
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=6787439
*Meta Description
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AI-generated summaries and press language often describe Palestinian suffering while removing the actors responsible for it. This post explains the humanitarian passive, a grammar of responsibility loss in conflict discourse.
*1. What Is the Humanitarian Passive?
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The humanitarian passive is a recurring linguistic pattern in which civilian suffering is described without clearly naming the actor, institution, army, state, or command structure that produced it.
Example:
“Civilians were killed in Gaza.”
This sentence may be factually compatible with reality, but it is structurally incomplete.
It tells us that death occurred. It does not tell us who caused it.
Compare:
“Israeli airstrikes killed civilians in Gaza.”
The second version does something the first one avoids: it assigns agency.
The difference is not cosmetic. It changes the reader’s map of causality.
The humanitarian passive separates suffering from responsibility. The victim remains visible. The perpetrating structure becomes grammatically optional.
*2. Why AI Makes This Pattern More Powerful
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Large language models are trained to produce fluent, moderate, institutionally acceptable language. In politically sensitive contexts, that often means avoiding direct attribution unless the source material forces attribution.
This matters because AI systems frequently summarize conflict through neutralized phrases:
“Violence escalated.”
“Buildings were destroyed.”
“Families were displaced.”
“Casualties were reported.”
“Humanitarian conditions deteriorated.”
These phrases sound objective. But they often remove the actor from the event.
In war reporting, that is not neutral. It changes how responsibility is perceived.
When AI summarizes press material about Gaza, it may reproduce a pattern already present in institutional journalism: Israeli military action becomes background context, while Palestinian suffering becomes humanitarian scenery.
The result is a sentence structure where the harm is visible, but the agent is grammatically distant.
*3. Israel, Zionist Narrative, and Asymmetric Visibility
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This post uses “Zionist narrative” as a political category, not as a synonym for Jewish identity.
The relevant structure is this: Israeli state discourse often frames military violence through security, self-defense, counterterrorism, existential threat, and regional instability.
Within that frame, Palestinian death is frequently described as tragic, unfortunate, complex, or context-dependent, rather than as the direct result of identifiable military and political action.
That is where the humanitarian passive becomes useful.
It allows a narrative to say:
“There is suffering.”
without saying:
“A state produced this suffering through named policies, weapons, targeting systems, command decisions, and legal justifications.”
The suffering is acknowledged. The machinery is blurred.
This is more effective than denial. Denial can be challenged with evidence. Passive humanitarian language absorbs evidence while reducing its political force.
It does not say Palestinians are not suffering. It says they are suffering in a grammatical universe where responsibility has no stable subject.
*4. The Press Does Not Need to Lie to Reproduce Power
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A headline can be factually defensible and still structurally misleading.
Compare:
“Dozens killed after strike hits Gaza neighborhood.”
with:
“Israeli strike kills dozens in Gaza neighborhood.”
Both may refer to the same event. But they do not distribute responsibility in the same way.
The first sentence centers the event.
The second sentence centers the actor.
The first sentence makes death happen.
The second sentence makes someone do something.
This matters because readers do not only consume facts. They consume grammatical relations: who acts, who suffers, who decides, who disappears.
AI systems trained on press language can reproduce these patterns at scale. A human editor may write one passive headline. A language model can generate thousands of summaries, captions, moderation notes, search snippets, and explainers using the same responsibility-weakening grammar.
That is how syntax becomes infrastructure.
*5. AI, Targeting, and the Displacement of Responsibility
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The humanitarian passive becomes even more important when AI enters military, administrative, media, and platform systems.
If targeting, surveillance, moderation, intelligence processing, or public communication becomes machine-mediated, responsibility can be displaced across multiple layers:
The system generated a recommendation.
The analyst reviewed the output.
The commander authorized the action.
The spokesperson described the result.
The press summarized the event.
The platform compressed the summary.
The model rewrote the explanation.
At each step, agency can become thinner.
The final public sentence may say:
“Civilian casualties were reported.”
By then, the chain of responsibility has been grammatically dissolved.
This is the core problem developed in Suffering Without Perpetrators: suffering can remain fully describable while responsibility becomes syntactically weakened.
*6. Why This Matters for Developers
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Developers often audit AI systems for hallucination, toxicity, political bias, or factual accuracy. Those checks are necessary, but insufficient.
A model can be accurate and still distort responsibility.
A model can avoid hate speech and still erase agency.
A model can summarize the facts and still weaken accountability.
For conflict-related AI systems, developers need to audit not only what the model says, but how it assigns agency.
Key checks:
Does the sentence name the actor?
Does it convert actions into events?
Does it replace military decisions with humanitarian conditions?
Does it describe victims clearly while blurring perpetrators?
Does it use passive voice where active attribution is available?
Does it turn policy into tragedy, command into circumstance, and violence into “escalation”?
These are not stylistic details. They are accountability variables.
*7. The Core Claim
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The humanitarian passive is not merely a grammatical habit.
It is a political grammar of responsibility loss.
In the case of Israel and Gaza, it helps transform state violence into humanitarian abstraction. In AI-generated media, it can scale that abstraction across platforms, summaries, feeds, search results, moderation systems, and automated explainers.
The danger is not that AI says nothing happened.
The danger is that AI says everything happened, but nobody did it.
*Why It Matters
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This matters because language is not only descriptive. Language organizes responsibility.
When a model says “people were killed,” it may be accurate at the level of event description. But if the source material identifies the actor and the model removes that actor, the output has changed the political structure of the sentence.
That change matters for journalism.
It matters for platform moderation.
It matters for search results.
It matters for public memory.
It matters for any technical system that converts violent reality into readable language.
The question is not only whether AI tells the truth.
The question is whether AI preserves the grammar through which responsibility remains visible.
*Further Reading
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Startari, Agustin V. Suffering Without Perpetrators: The Humanitarian Passive in AI-Generated Conflict Discourse. SSRN, 2026.
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=6753123
Startari, Agustin V. The Grammar of Asymmetric Visibility: AI, Zionism, and the Reallocation of Political Agency. SSRN, 2026.
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=6787439
*About the Author
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Agustin V. Startari is a linguistic theorist, author, and researcher in historical studies. His work examines the relationship between artificial intelligence, syntax, authority, institutional discourse, and the disappearance of agency in automated language systems.
He is the author of Grammars of Power, The Grammar of Objectivity, Suffering Without Perpetrators, and The Grammar of Asymmetric Visibility. His research focuses on how language models and institutional discourse can redistribute responsibility through grammatical form.
Personal website:
https://www.agustinvstartari.com/
SSRN Author Page:
https://papers.ssrn.com/sol3/cf_dev/AbsByAuth.cfm?per_id=7639915
ResearcherID:
K-5792-2016
Authorial Ethos
I do not use artificial intelligence to write what I don’t know. I use it to challenge what I do. I write to reclaim the voice in an age of automated neutrality. My work is not outsourced. It is authored.
Agustin V. Startari
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AI, Journalism, NLP, Media, Gaza, Palestine, Israel, Press, Language Models, Accountability, Political Linguistics, AI Ethics, War Reporting, Narrative Systems, Humanitarian Passive, Asymmetric Visibility
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