AI Misalignment: When Following the Rules is Not Enough!

This is Decoded.

AI for everyone.

The podcast that makes sense of artificial intelligence.

Not just in your pocket, but in your work, your choices, your creativity, and your everyday life.

I'm Joel Leslie.

I work in AI research, AI governance, AI cybersecurity, and AI real-world adoption, helping organizations better understand how to use these systems safely, effectively, and without creating future risk.

On this show, I take those ideas and bring them back to everyday life, so AI feels less confusing, less distant, and a lot more useful for everyone.

We need to find the patients most likely to need extra support.

Start with the highest risk group first.

Care management list generated.

Priority ranking complete.

Hang on.

This patient was in three times last month.

Why are they below the line? The system has ranked them by predicted future cost.

But lower cost does not mean lower need.

That is one of the hardest things to understand about AI.

Sometimes the problem is not that the system breaks the rules.

Sometimes the problem is that it follows the rules too well.

It follows the written instruction.

It optimizes the target, completes the task and produces the list.

It gives the answer.

It meets the metric and still misses the human purpose.

That is misalignment.

Not robots becoming conscious.

Not science fiction.

Not an evil machine deciding to rebel.

Misalignment can be much simpler than that.

It is what happens when the instruction and the intent are not the same thing.

When the measurable target becomes more important than the real world purpose.

When the system learns what gets rewarded, but not why the reward exists.

When the AI does what we said, but not what we meant.

This is Decoded, AI for everyone.

I'm Joel Leslie, and this is season four, Applied Intelligence.

In the last episode, we looked at AI security.

The idea that the model, the data it can see, the tools it can use, and the context around it all need protection.

The danger was not only that someone could break into a system, it was that someone could manipulate the AI into using its own access in the wrong way.

This episode is different.

There does not need to be an attacker.

There does not need to be a malicious prompt or a breach.

The system may be working exactly as designed.

And that is the problem.

because AI systems do not automatically understand human purpose.

They respond to instructions, they optimize goals, they learn patterns, follow incentives, and they produce outputs.

But they do not always understand the reason the system exists.

And when the written goal is too narrow, the AI may succeed at the wrong thing.

That is what makes this episode different from security.

Security asks, what if someone turns the system against us? Misalignment asks, what if we accidentally point the system at the wrong target? A powerful, real-world example comes from healthcare.