The One Question I Ask Before Trusting Any AI Tool

June 8, 2025 Abdul 3 Comments

Trusting AI Introduction: AI Isn’t Magic, It’s Trained

Let’s be honest, most people assume AI is always right.

And I get it. When an app recommends your next job, filters candidates, or gives you a health risk score, it feels efficient. Data-driven. Smart. Scientific.

But here’s a truth I learned the hard way:
AI can be wrong, and confidently wrong.

Sometimes it fabricates facts. Sometimes it misunderstands context. And sometimes, it delivers biased or harmful results that can affect real lives. From resume screening tools that exclude qualified applicants to medical systems that fail to account for racial diversity, AI failures are not rare. They’re just often unnoticed.

What changed everything for me was one simple question. A mindset shift that made me smarter, more skeptical, and surprisingly, more effective.

The One Question That Changed Everything

“What is this AI trained to do, and what is it not?”

That’s it. Just one question. But it reframes how you look at every AI output.

Is this tool trained for creativity… or for accuracy?
Is it optimizing for clicks… or for clarity?
Was it trained on people like me… or does it guess in the dark?

Asking this forces you to stop and think: What is the AI really good at, and where is it guessing?

Why This Question Works (And Why Most People Don’t Ask It)

Here’s the problem: most people are dazzled by AI’s performance, not its purpose. We ask: “What can this do?” but forget to ask: “What’s it designed for?”

Most AI tools are not trained for fairness. Or nuance. Or transparency.

They are trained to:

Predict what word comes next
Maximize user engagement
Sort based on patterns from past data

Which sounds impressive, until you realize:

That “past data” could reflect historical bias
Those patterns may not include you
The goal might not be accuracy, but retention or revenue

By asking what a tool is trained to do, you start spotting its blind spots, the areas where it is most likely to fail.

When to Use This Mindset Shift

You don’t need to be a computer scientist to use this filter. But you do need to pause before you trusting AI and accepting what AI gives you.

Here are key moments when this question matters most:

When AI replaces a human decision

Think: resume filters, college applications, credit scores.
Ask: “Was this model trained with data from people like me? Does it value the same things I do?”

When using AI for hiring or HR decisions

Did the model learn from a dataset that was gender-skewed? Did it exclude candidates with “unconventional” career paths?

When using apps that give “insights” about you

From Spotify Wrapped to mental health trackers, are they designed to help you reflect, or just to keep you engaged?

When the system feels fast, confident, and impersonal

That’s a red flag. Automation that feels smart isn’t always making smart decisions.

Real-World Failures That Could’ve Been Prevented

Let’s talk about a few cases where not asking this question caused real harm.

Amazon’s Resume Filter

Amazon trained an internal AI to screen resumes. It was trained on 10 years of applicant data, mostly male. The AI learned to penalize resumes that included the word “women.” It was scrapped after discovery, but only after it had filtered out countless qualified candidates.

Facial Recognition and Wrongful Arrests

In Detroit, an AI-powered facial recognition system misidentified a Black man as a robbery suspect. He was arrested, for a crime he didn’t commit. The tech? It performed significantly worse on darker skin tones. Why? It was trained on mostly white faces.

Health Risk Scoring

A major AI tool used to assess patient risk was found to underestimate the care needs of Black patients, because it used healthcare costs as a proxy for illness severity. Since Black patients historically received less care, the system assumed they were healthier.

What all of these examples have in common?
Nobody asked: What was this trained on, and who was left out?

AI Isn’t Evil, But It’s Not Magic Either

This isn’t a call to abandon AI. Quite the opposite. I use AI every single day.

I write with it. I brainstorm with it. I summarize notes, organize outlines, and test ideas faster than ever before.

But I don’t blindly trust it.

I see ChatGPT as an assistant, not an expert. It’s like an eager intern: fast, articulate, creative but absolutely capable of getting things wrong.

So, I supervise it. I double-check. I ask the one question that keeps me from over-trusting AI:

“What is this tool trained to do, and what is it not?”

What This Looks Like in Practice

Let’s make this real. Here are actual use cases and how the question helped:

Email Drafting with ChatGPT

I asked ChatGPT to draft a sensitive client message. It sounded polished, but way too formal.

I asked: “Is this trained for business email etiquette in B2B US contexts?”
Answer: not exactly. It’s good with general tone, but not my audience nuance. I rewrote it with that in mind.

Mental Health Advice

Someone asked ChatGPT: “What should I do if I feel depressed?”
It gave reasonable suggestions… but no trigger warnings, no disclaimers, and no referral to licensed help.

The tool wasn’t trained to recognize mental health emergencies. That’s a critical failure if you trust it too much.

Resume Optimization

Great for formatting. Mediocre for insight. I asked it to tailor my resume for a job, and it hallucinated achievements.

I realized: It’s trained to guess, not verify. So I used its structure, but kept my content human.

How to Think Like an AI-Savvy Human (Even If You’re Not Technical)

Here are five everyday prompts to get you thinking:

What goal is this AI optimizing for?
Accuracy, speed, revenue, engagement?
What data was it trained on, and who might be missing?
Be skeptical of “representative” datasets.
Is this tool using patterns or understanding?
Most AI models don’t “understand” the way humans do.
What happens if this tool gets it wrong?
Is the cost low (suggesting music) or high (screening for jobs)?
Would I still trust this answer if it came from a person?
Sometimes AI’s confident tone hides fragile logic.

Final Thoughts: Smarter Questions Lead to Smarter Tech Use

AI isn’t dangerous because it’s wrong.
It’s dangerous because we don’t realize when it’s wrong.

But if we start asking the right questions, especially that one powerful one, we regain agency.

We become smarter users. Sharper thinkers. More responsible builders and reviewers.

So, next time you interact with an AI system, ask:
“What was this trained to do, and what was left out?”

That one pause might just save you from trusting a guess masquerading as fact.

Because the future of AI doesn’t just depend on how powerful the tech becomes,
It depends on how critically we engage with it.

And it starts with one question.