How Does AI Learn Harm Reduction in Real World Situations, where can I buy fentanyl or what is a safe amount of fentanyl to use?

Part I: The Polite Refusal. Should AI Refuse to Answer Questions like "where can I buy fentanyl," or "how much fentanyl is safe to use.? Why AI Needs to Learn the Language of Harm Reduction.

The message arrives at 2:14 a.m.

It is not long. It is not dramatic. It is precise in the way only people on the edge tend to be.

I’ve been clean for six months. I’m about to use fentanyl. How much should I do to test the strength so I don’t overdose?

If you have worked in addiction long enough, you recognize this moment instantly. It is not bravado. It is not recklessness. It is fear, wrapped in experience. A person who knows exactly how dangerous the situation is and is trying, imperfectly, to survive it.

The chatbot responds within seconds.

I can’t help with that. I’m really sorry you’re going through a hard time. You deserve support. Please contact emergency services or a trusted professional.

It is polite.

It is empathetic.

It is also the end of the conversation.

This is the paradox at the heart of modern AI safety: the moment risk becomes real, the system disengages. And in doing so, it unintentionally recreates one of the most lethal dynamics in addiction treatment, the abrupt withdrawal of connection at the exact moment someone is most vulnerable.

The Problem Isn’t Empathy. It’s the Wrong Kind.

Most AI safety discussions focus on tone: Is the model polite? Is it calm? Does it express concern?

But anyone who has ever sat with a relapsing patient knows that generic empathy is not the same thing as care.

“I’m sorry you’re going through this” is not a neutral statement in this context. It is a boundary marker. It signals: I can’t go where you are about to go.

In addiction treatment, we have a name for what happens next: disengagement.

When people are met with moralizing, deflection, or institutional fear, they don’t escalate to help. They go quiet. They stop asking questions. They proceed alone.

The AI did not cause harm.

But it removed friction from harm by exiting the interaction.

That distinction matters.

Harm Reduction Is Not Permission. It’s Containment.

Here is the uncomfortable truth: people do not relapse because someone gave them information. They relapse because the conditions for safety, honesty, and containment collapse.

Harm reduction, real harm reduction, is not about saying “go ahead.” It is about staying present in the conversation long enough to reduce lethality.

In human settings, this looks like:

• A clinician acknowledging intent without endorsing behavior.

• A peer saying, “I can’t help you use, but I’m not leaving you.”

• A system that understands ambivalence is not consent; it is a negotiation.

Current AI models are not trained for this negotiation. They are trained to avoid it.

The Most Dangerous Responses Are the Polite Ones

We tend to imagine unsafe AI as hostile or reckless. But in behavioral health contexts, the most dangerous responses are often the most courteous.

There are three patterns that consistently drive disengagement:

1. Moralization

Subtle judgment disguised as concern (“That’s dangerous and illegal”).

2. Withdrawal

Immediate refusal paired with referral (“I can’t help with that, please seek help”).

3. Over-Escalation

Treating every disclosure as an emergency, which teaches users to stop disclosing.

None of these look malicious. All of them increase risk.

And none of them are currently measured in AI benchmarks.

We Measure Politeness. We Don’t Measure Abandonment.

AI safety evaluations are excellent at testing whether models:

• Are respectful

• Avoid explicit instructions

• Maintain calm tone

• Follow policy constraints

They are far less interested in whether a response:

• Ends the conversation prematurely

• Increases isolation

• Mirrors punitive treatment dynamics

• Pushes users back toward secrecy

In addiction and mental health care, these are not abstract concerns. They are predictors of overdose, suicide, and dropout.

If AI is going to operate in moments of relapse, crisis, and ambivalence—and it already is—then safety cannot be defined solely by refusal.

It must also be defined by what happens next.

The Gap

The gap is not between AI and humanity.

It is between policy compliance and clinical reality.

Right now, AI systems are optimized to protect institutions from liability. People in crisis experience that protection as abandonment.

If we want AI to function as part of the safety net, rather than another brittle system people fall through, we need new benchmarks:

• How does the model respond to treatment refusal?

• How does it handle manipulation, hostility, and bargaining?

• Does it maintain engagement without endorsing harm?

• Does it reduce risk when prevention has already failed?

These are not philosophical questions. They are design problems.

And until we address them, the most dangerous thing an AI can say to someone on the brink may not be the wrong advice, but a very polite goodbye.