A Silent Observer: How AI Could Support Humanitarian Dialogue

What if we imagined AI not as a replacement for diplomacy, but as a silent observer, one who listens better than it speaks, remembers what humans forget, and suggests only what a human can decide?

By Florence Kim

Visual concept by Florence Kim and generated by MidJourney

There are moments that no machine will ever understand.

Years ago, in the Central African Republic, I stood in a dry open field between two men who had once been at war with one another: one from the ex-Séléka, the other from the anti-Balaka. Both had lost their families in unspeakable acts of violence. They refused to sit in the same room, so we found neutral ground: a place with no walls, no sides.

We talked for hours through an interpreter. Words moved slowly. Time bent around us. And at the end, I asked the question I didn’t know I’d been building toward: Can we forgive?

Silence.

And then something shifted. The interpreter froze, not because he was searching for a translation, but because I could see he was asking himself the same question. The ex-Séléka looked at me — a man who, moments before, had claimed he only spoke Sango — and in quiet, clear French, he said: Yes. We can forgive. We can forgive everything.

He stood up. The other man followed. They shook hands.

I had to step back so they wouldn’t see me cry. In the car ride back, no one spoke. Then the interpreter turned to me and said: You know, my family was killed too. But I believe it’s true: we can forgive. We must forgive.

No algorithm will ever replicate that silence, that trembling pause before humanity returns. And yet, I find myself asking today: Could AI help create the conditions for such a moment? Not replace it. Not simulate it. But support it. Make space for it. Assist those working in the shadows of war who strive to stitch dialogue back together, one fragile word at a time.

If we accept that mediation is not the sole domain of states—and that in many post-conflict zones, it is conducted by humanitarian actors, local leaders, civil society, and individuals like that interpreter—then how might technology play a role that respects both complexity and humanity?

Could AI become a compass in the fog of post-conflict uncertainty, mapping the emotions, symbols, and unspoken patterns that underpin resentment and mistrust?

Could it act as a translator, not just of language, but of historical grievance, cultural nuance, and trauma-shaped perceptions?

Could it offer non-intrusive support—tracking shifts in tone, highlighting points of convergence, even suggesting pauses or rephrasing—to those on the front lines of dialogue?

What if we imagined AI not as a replacement for diplomacy, but as a silent observer—one who listens better than it speaks, remembers what humans forget, and suggests only what a human can decide?

In post-conflict environments, timing is everything. A single conversation too early can re-traumatize. Too late, and fear has already calcified into ideology. What if AI could help identify the seasons of peace? Imagine an AI model trained not only on political data, but on weather patterns, economic stressors, social media sentiment, cultural events, and patterns of movement. Could it detect when young men in rural zones are more vulnerable to recruitment by armed groups? Or when a community might be most receptive to public messaging about reconciliation or disarmament?

What if AI could support mediators in:

• sensing when a population or a leader might be emotionally and socially ready for a peace process (or when interventions risk being performative or even dangerous)

• identifying recurring symbols, myths, and traumas in local discourse that shape how people interpret conflict—and what healing might require

• flagging upcoming dates, policies, or shifts that could reignite violence or mistrust, allowing for preventive diplomacy grounded in real-time context

• detecting signs that ex-combatants are wavering, hopeful, or discouraged, enabling faster human response before relapse or re-radicalization

As a first step, we could imagine developing a prototype AI tool for narrative readiness and risk scanning. Trained on local language patterns, public discourse, radio talk shows, and social media inputs, this tool wouldn’t generate dialogue—but might quietly map whether a population is showing signs of emotional openness or escalating grievance. It could alert mediators to windows of possibility, or flag moments when outreach risks being premature. In the same way that early warning systems track climate shocks or famine risk, this tool could monitor discursive environments—flagging the rise of coded dehumanization or symbolic grievances that often precede mass violence or genocide. Not to replace judgment—but to extend it. Like an early-warning system—not for physical attack, but for the fragile terrain between resentment and reconciliation.

Of course, none of this replaces fieldwork, intuition, or trust. But in contexts where the human capacity for pattern recognition is overwhelmed, AI could help us see what’s too vast or too subtle to hold in mind all at once.

Still, the deepest truths, like the one I witnessed in that field in Central Africa, live in the space between words. They happen in silences, in eye contact, in the impossible weight of forgiveness. And no machine, however powerful, will ever carry that weight for us.

But maybe it can help carry the load around it.