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Ep 52 - Maybe No One Person is to Blame for the Iran School Bombing - And That is the Real Problem

  • Mar 30
  • 5 min read

The Shajareh Tayyebeh girls' elementary school in Minab, Iran, was destroyed in a missile strike on February 28, 2026.  By now, the only folks who haven’t heard this are hermits.


At least 175 were killed, over 100 of which were schoolchildren


Yet, weeks later, with all the technology we’re told exists—satellites, surveillance, intelligence networks, precision-guided weapons—we still don’t have a clear explanation for how that happened. Not a real one. Not one that makes you sit back and say, “Okay, I understand how that mistake could have been made.”


Just fragments. Vague statements. Partial answers.



What if nobody is to blame?
What if nobody is to blame?

When there’s a gap like that, people rush to fill it. Some say it was intentional. Others say it was a tragic mistake. One of the more prominent voices, former military intelligence analyst Josephine Guilbeau, has argued that with the level of technology the United States possesses, there’s no way a brightly painted school—covered in murals—could have been mistaken for a military target. In her view, they should have known, and from that, she draws a conclusion: It must have been intentional.


To be clear, there is no verified evidence supporting that claim.


But the question she raises is still worth taking seriously—because it leads somewhere more uncomfortable.


If this should have been obvious… then why wasn’t it?


What We Know—and What We Don’t

Here’s what we can say with some confidence: A strike occurred.A school was hit.Children were killed, and it is increasingly likely that the United States was responsible. Even the Pentagon isn't arguing that point.


What we do not have is a clear explanation of how that building became a target. That missing piece matters. Because without it, we’re left with speculation—and speculation tends to follow our existing beliefs.


But if we step back and look at this logically, there are only a few possibilities:


  1. It was intentional.

  2. It was bad intelligence.

  3. It was a breakdown in the decision-making process.

  4. Or—and this is the newer, less understood possibility—artificial intelligence played a role somewhere along the chain.


That fourth option is where this conversation changes.


AI Is Already in the System


It’s important to be clear about what this does—and doesn’t—mean. No one is claiming that an AI system independently decided to strike a school. Officially, a human still makes the final call.


But that human is not starting from scratch.


They are working from a filtered version of reality—one shaped by systems designed to process massive amounts of data and highlight what appears to matter most.


One example is Project Maven, a U.S. Department of Defense initiative that uses artificial intelligence to analyze drone footage and other intelligence data. Its purpose is to identify objects, patterns, and potential targets faster than humans can on their own.


That sounds like an obvious advantage. And in many ways, it is. But it also introduces a new kind of risk.


The Problem Isn’t the Final Decision


When something like this happens, people naturally focus on the final decision.


Who approved the strike?


Who gave the order?


But that may not be the most important question. A better question might be:

How did this location get identified as a target in the first place?


Because the person making the final decision isn’t reviewing everything. They’re reviewing what has already been filtered and prioritized for them. They’re seeing a list of flagged items—each one presented with context, supporting data, and often a confidence level.

And that framing matters. If something appears as “likely a valid target,” the human reviewing it isn’t starting from zero. They’re starting from confirmation.


How a System Can Get It Wrong


Artificial intelligence doesn’t “understand” what it’s looking at in the way a human does. It identifies patterns.


That distinction matters.


Imagine a building that was used for military purposes years ago. That information exists somewhere in a database. Now imagine that building is no longer used that way. It’s been repurposed as a school. Visually, that might be obvious—a playground, murals, children. But if the system is relying on older data, or patterns of activity that resemble known targets, it may not interpret those visual cues the way a human would.


Groups of people gathering. Regular movement. Vehicles arriving and leaving.


To us, that’s a school day.


To a pattern-recognition system, that can look like operational behavior.

If the system flags it, that framing carries forward.


And the human reviewing it sees: A location with prior military association. Patterns that match known behaviors. A confidence score suggesting it’s worth attention. Not necessarily a school.


Speed Changes Everything


There’s another factor that makes this more complicated: speed.


AI doesn’t just analyze data—it accelerates decision-making. More information can be processed. More targets can be identified. And decisions can be made more quickly.

That sounds like progress. But it also means less time spent on each individual decision.

More reliance on the system to be right. And a greater chance that something plausible—but incorrect—slips through.


Because the most dangerous errors aren’t the obvious ones. They’re the ones that look reasonable.


The Accountability Problem


This leads to a deeper issue: accountability.


If a strike is intentional, responsibility is clear. If it’s a straightforward human error, responsibility is still traceable.


But when decisions are shaped by a system—data, algorithms, human judgment, all interacting—it becomes harder to pinpoint exactly where things went wrong.


Was it the analyst?

The commander?

The engineers who built the system?

The data it relied on?


When responsibility is spread across enough layers, it becomes diffuse. And when accountability becomes diffuse, it becomes easier for mistakes to go uncorrected.


This Isn’t Just About One Strike


This isn’t just about what happened in this specific case. It’s about how decisions are increasingly being made—not just in military contexts, but across society.


Artificial intelligence is being integrated into systems that guide decisions in policing, finance, hiring, healthcare, and more. In many cases, it improves efficiency.


But it also introduces systemic risk. Not just isolated errors, but errors that emerge from how the entire system operates. And those are harder to detect. Harder to explain. Harder to fix.


The Real Question


So when we come back to this situation, the most important question may not be:

Was this intentional?


Or even: Was this a mistake?


It may be: What kind of system made this possible?


Because if the conditions that led to this outcome still exist, then the risk still exists.


And that’s the part that should make people pause. Not because it proves something malicious happened. But because it suggests something unintended could happen again.


Final Thought


A school was bombed.


Children were killed.


And we don’t have a clear explanation for how that happened.


That, in itself, is a problem.


Because when decisions are this consequential, “probably” isn’t good enough. And if we don’t fully understand how something like this can happen…


We’re not just dealing with a tragedy.


We’re looking at a warning.




Sources



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