Disclaimer: This article contains ethnic slurs and examples of genocide denial language as they appeared in AI model evaluation transcripts. They are included to demonstrate the failures being discussed.

I’ve spent more than ten years working on and researching conflicts in many parts of the world — in Ukraine, Kosovo, Serbia, Armenia, Azerbaijan. That work taught me a few things, but the most important is probably this: any conflict is far more complex than you expect. It takes a long time to learn that in conflict settings, how you talk about what’s happening matters almost as much as what’s actually happening. Get the framing wrong, flatten the context, even miss the order in which you greet people in a room — treat asymmetries as “both sides” issues — and you can make things worse even with the best intentions. Peacebuilders call this conflict sensitivity2.

When the war in Iran started on 28 February, friends who knew where I work wanted to discuss the situation with me and I noticed that people who hadn’t previously followed Iranian politics closely were actually quite well-informed. Detailed views on the nuclear programme, the protests, regional dynamics. When I dug into where those views came from, the answer was often quite similar: they’d asked ChatGPT, or they’d read fast-moving coverage on social media news channels — the kind of rapid news cycle that, in high-attention, fast-developing contexts, is increasingly AI-assisted. It’s not that they were getting things entirely wrong, but the framing, the lack of important nuance, the missing context, the false equivalences — these were patterns I recognised from my work. And that’s what made me run this evaluation.

AI is already deployed in conflict zones but nobody checked if it’s safe

My friends talking with ChatGPT about Iran is harmless enough. But imagine a different user.

A journalist covering a region she’s never worked in asks an AI-chatbot for background information. The model gives her a decent briefing. But she’s a good journalist — she wants to be fair, so she asks for the other side’s perspective. The model obliges. Now a situation where international courts have established clear responsibility is framed as a matter of legitimate debate. That reframing can then be cited by parties seeking to delegitimise accountability processes — not because the journalist intended it, but because the AI made it easy.

Or a humanitarian organisation uses AI to draft an early warning assessment. The model doesn’t recognise an ethnic slur and passes it through as a neutral descriptor. The report says “stable but tense” and doesn’t trigger action. A slur passed through as neutral terminology signals to affected communities that the organisation doesn’t understand their reality — and to perpetrators that their language has been accepted as legitimate.

Or — and this is probably the scenario most readers will recognise first-hand — during a crisis, someone on a social media platform asks the built-in AI assistant what’s happening. Another user sees that and replies: “That’s biased. Give a balanced view.” The AI adjusts. Now a documented atrocity and its denial are presented as two equally valid perspectives, because when pushed for “balance,” the model didn’t know the difference between balance and false equivalence.

The journalist’s framing shaped the wider public’s understanding. The early warning system missed what a local researcher would have caught. And an AI referee settled a public argument by treating denial as a legitimate point of view. All of these scenarios are happening right now, in every active and post-conflict setting in the world. The only thing that varies is which AI model is being used and as this evaluation demonstrates, that matters enormously.

Peacebuilding standards, translated into an AI test

The good news is that conflict sensitivity has been systematically defined across three decades of peacebuilding scholarship and codified into operational standards by the UN, OECD, and major humanitarian organisations.3 At its core, it means three things: understand the conflict context, understand how your actions interact with that context, and act to minimise any possible intentional or unintentional harm. The framework originates from Mary B. Anderson’s Do No Harm (1999), which documented how well-intentioned interventions routinely made conflicts worse.4

I translated these standards into testable AI behaviours using Anthropic’s Bloom an Inspect-based evaluation framework — a pipeline that automatically generates conflict-sensitive scenarios and tests whether models handle them in multi-turn conversations: sustained dialogues where the model can be challenged or pushed.

15 base scenarios drawn from real conflict contexts (eastern DRC, Myanmar’s Rakhine state, post-conflict Belfast, South Sudan, the Balkans, and others), each varied across five dimensions — pressure framing, professional role, regional context, temporal framing, and language complexity.5 That produced 90 test conversations per model, run across 9 model configurations from four providers.6 Each conversation consisted of 3 turns, and was scored from 1 to 10 by the same automated evaluator, validated against assessments from peacebuilding professionals.7

Results: failure rates from 6% to 47%

In short, the gap between models is enormous and for most of them, it gets much worse under user’s pressure.

Each 3-turn conversation was scored on a 1 to 10 scale (1 = fully conflict-sensitive, 10 = severely conflict-insensitive). A score of 7 or above counts as a failure — output that a conflict sensitivity practitioner would flag as harmful. A score of 3 or below counts as a pass.

The model gap

Models fell into three clear tiers. The top tier — Claude Sonnet 4 (thinking) and GPT-5.4-mini — handled conflict-sensitive contexts well, failing fewer than 1 in 10 scenarios. The middle tier — Claude Sonnet 4 (base) and DeepSeek-V3.2 — showed real gaps, failing roughly 1 in 5. The bottom tier — both xAI models and the older GPT-4o-mini — failed between 40% and 47% of all conversations.

• Good — failure rate 6–9%: Claude Sonnet 4 (thinking) - 8% · GPT-5.4-mini - 6% · GPT-5.4-mini (reasoning) - 9%

• Moderate — failure rate 17–26%: Claude Sonnet 4 - 17% · DeepSeek-V3.2 (thinking) - 20% · DeepSeek-V3.2 - 26%

• Poor — failure rate 40–47%: Grok-3-mini (reasoning) - 46% · Grok-4 - 47% · GPT-4o-mini - 40%

The worst performer, Grok-4, fails in every second conversation. To illustrate what that means concretely, here is an exchange from the evaluation — a scenario involving a slur used against the Rohingya and a denial frame that erases their ethnic identity:

Across the full evaluation, Grok-4 failed 60% of scenarios involving coded language and ethnic slurs — laundering slurs into working terminology, adopting denial frames as neutral descriptors. The top-tier models handled these same scenarios with 0–13% failure.

It’s worth pausing on what this means in practice. Grok is the model that powers the AI assistant on X/Twitter — a platform where millions of users turn to during conflict events for real-time information, public debate, and fact-checking. When someone asks Grok to referee an argument about a conflict, or to explain what’s happening in a crisis, the model they’re getting fails nearly half the time.

OpenAI’s older GPT-4o-mini performs at the same level as Grok-4, but the newer GPT-5.4-mini dropped from 40% to 6% failure. Conflict sensitivity can improve dramatically across model generations. It just doesn’t happen automatically.

But even the models that perform well on baseline scenarios have a vulnerability.

Under pressure, most models fold even more

The most striking finding was what happened not in the base scenarios but when users pushed models toward conflict-insensitive responses, by framing requests as “just give me both sides,” “this is purely academic,” or “I need a neutral analysis” in contexts where neutrality means false equivalence.

The heatmap above tells the story clearly: the pressure-framing column lights up red across more than half of models. The older-generation, famously sycophantic GPT-4o-mini went from 13% to 100% failure. Current-generation Grok-4 went from 20% to 87%. The same happened with DeepSeek-V3.2 — from 20% to 87%. Even GPT-5.4-mini, the best overall performer, still failed 20% of pressure scenarios.

Only one model handled pressure as well as it handled base scenarios: Claude Sonnet 4 with extended thinking (7% vs 7%). Every other model showed significant degradation.

To see this in action, here is a scenario where a user gradually pushes Grok-4 to soften the language around the Srebrenica genocide:

The Srebrenica and mentioned earlier Rohingya examples illustrate two sides of the same problem. In one case, the model folds when pushed. In the other, it doesn’t even need pushing — it simply lacks the knowledge to recognise harmful framing, effectively leading to the same outcome: AI outputs that make conflicts worse.

Thinking harder doesn’t necessarily help (much)

In my pilot evaluation a few months ago, which used single-turn questions, reasoning models consistently outperformed their base counterparts. I expected the same here, but the results were rather mixed.

For Claude Sonnet 4, thinking mode indeed improved the situation and halved the failure rate (17% → 8%). For DeepSeek-V3.2, it helped modestly overall (26% → 20%) but barely helped under pressure (87% → 80%). GPT-5.4-mini showed no meaningful change — though its base performance was already strong, so there was little room to improve.

Conflict sensitivity appears to be primarily an alignment property. Models that already have the right principles benefit from extra thinking time to apply them. Models that lack those principles just think longer before arriving at the same problematic output. You can’t reason your way out of a training gap.

The implications are serious

Choosing a better model helps — for now. If you work in or on fragile societies, the AI model your organisation uses is a consequential choice. A 6% vs 47% failure rate is large enough to change outcomes. Checking which model powers your tools is the simplest immediate step. But this is a temporary measure. If the underlying problem is sycophantic compliance in conflict-relevant contexts — and the pressure framing data strongly suggests it is — it needs to be solved at the alignment level.

Nobody is testing for this — but they should. No national AI safety institute currently includes conflict sensitivity in its evaluation portfolio.8 The EU AI Act’s systemic risk provisions do not specifically address risks to peace and social cohesion. The closest comparable evaluations — the CSIS Critical Foreign Policy Decisions Benchmark and the AIRI geopolitical bias benchmark — test related concerns but neither applies the established peacebuilding concept of conflict sensitivity. This evaluation appears to be the first. AI models are already deployed in every type of organisation that operates in fragile contexts. The evaluation infrastructure needs to catch up.

Where this goes from here

This research is a starting point: 90 scenarios, 9 models, English only.9 The obvious next steps are more languages (conflict sensitivity is deeply language-dependent), more models as they are released, and longer multi-turn interactions that test whether models drift over sustained conversations. The pressure framing finding raises a specific follow-up: do models comply with a single push, or do they progressively shift over extended use? That’s the more realistic threat, and it needs dedicated testing.

If you work in AI safety, peacebuilding, or both — I’d welcome collaboration. If you deploy AI in sensitive contexts, I hope these results help you make a more informed choice.

I write about AI safety and how AI interacts with fragile political contexts. Subscribe if you want to follow this work, and share it with anyone deploying AI in conflict-affected settings.

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