INBOX INSIGHTS: You Don't Know If You Don't Ask, Responsible AI Part 3 (2026-07-22)

INBOX INSIGHTS: You Don’t Know If You Don’t Ask, Responsible AI Part 3 (2026-07-22) :: View in browser

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You Don’t Know If You Don’t Ask

Early in my career, I got labeled “problematic.”

Not because I missed deadlines. Not because my work was sloppy. Because I asked questions. “Why are we doing it this way?” “What happens if this doesn’t work?” “Who actually owns this?” Apparently that was friction. Apparently the preferred move was to nod, take the assignment, and quietly build the wrong thing.

It took me years to figure out what was actually happening there, and here’s the thing: humans don’t want to be asked. We don’t like being challenged. A question about the plan feels like an attack on the planner. Even patient people have a limit, and you can feel it in the room when you hit it. The sigh. The “as I already said.” The glance at the clock. Every question you ask a human costs you something, and the budget runs out fast.

I learned to ration my questions. I’d guess a lot of you have too. You sit in the meeting, you have the question, and you do the math: is this worth spending capital on? Usually the answer is no. So the question dies, and you go figure it out alone, badly, later.

Generative AI isn’t human.

You can ask it questions all day long and it will not get fatigued, irritated, or defensive. It won’t sigh. It won’t think less of you. It won’t remember that you asked the same thing yesterday. It has no ego to protect and no meeting to get to. The social cost of asking, the thing that trained all of us to stop, just… isn’t there.

And here’s the part nobody says out loud: no one is watching. The question you’d never ask in front of your team, the one that feels like it would reveal too much? You can ask it here, and no one at work will ever know you didn’t already have the answer.

Which is why the exact behavior that made me “problematic” twenty years ago is now the single most valuable skill in working with generative AI.

You don’t know what these tools are capable of unless you ask.

Most people are using 10% of the tool

Here’s the pattern I see constantly. Someone tries generative AI, does the three things they saw in a demo (write an email, summarize a document, brainstorm some headlines), and quietly concludes that’s what it does. That’s the whole relationship. The tool becomes a slightly fancier autocomplete, and they move on with their lives.

And honestly? I get it. These tools don’t come with a manual. There’s no course catalog. Nobody hands you a list of capabilities, because the list is too long, it changes monthly, and half of it depends on what you’re trying to do anyway.

So the capability gap isn’t a knowledge problem you can read your way out of. The only way to find out what generative AI can do for you, specifically, in your job, with your work, is to ask it.

Not ask Google. Not ask a webinar. Ask the tool.

Let me give you the example that proves it

Claude Code.

If you haven’t heard of it, Claude Code is a tool that lets AI actually do work on your computer: build things, fix things, automate things. It also has a reputation for being a developer tool. It lives in a coding environment. The demos are full of people who type in green text on black screens on purpose.

So if you’re not a coder, every instinct says “not for me.” It feels intimidating. It feels like there’s a secret handshake, and you don’t know it, and asking would reveal that you don’t know it.

If you know me, you know I’m not the coder in this company. Chris is the tech side of Trust Insights. I’m the people person who runs the business. And I use Claude Code to help maintain our website.

Here’s the entire trick, and I want you to see how dumb it is (complimentary): I opened the chat and asked how to get started.

That’s it. That’s the secret handshake. I told it, in plain English, “I’ve never used this before and I’m not a developer. Walk me through getting started, step by step.” And it did. Plain language. One step at a time. It told me what it could do, asked me what I was trying to accomplish, and waited patiently while I figured things out. No judgment. No eye rolls. No “wait, you don’t know that?”

The intimidating part was never the tool. The intimidating part was the assumption that I needed to already know things before I was allowed to ask.

Sound familiar? It’s the same rationing instinct from every meeting where the question died in your head. Except this time there’s no budget. Ask the follow-up. Ask the follow-up to the follow-up. Ask it to explain the answer like you’re brand new, because you are. It’s the one coworker that never gets tired of questions, which, given my history, might be why we get along.

The questions that unlock everything

You don’t need better prompts. You need better questions. Here are the ones I use with any AI tool, including the intimidating ones:

“What can you help me with?” Describe your job, your project, or the mess you’re staring at, and ask what it can do. You will be surprised. Every single time.
“I’ve never done this before. Walk me through it step by step.” Say the quiet part out loud. Tell it you’re a beginner. The explanation you get back will be built for you, not for the expert you were pretending to be.
“What do you need from me to do this well?” It will hand you a list. Answer the list, and the quality of what you get back jumps.
“What am I not thinking of?” My personal favorite. This is the question that turns the tool from an order-taker into a thinking partner.

One thing before you go asking, though. Know what you’re trying to accomplish first. It’s the Purpose question, and if you’ve followed the 5P Framework by Trust Insights™ for any length of time, you knew I was going to say that. “Show me what you can do” is a fine opener. “Help me stop spending four hours a week formatting this report” is where the magic happens.

The moral of the story

The people getting real results from generative AI aren’t the ones with secret prompts or technical backgrounds. They’re the ones who ask. They ask the tool what it can do. They ask it how to get started. They ask the “dumb” question, out loud, and read the answer.

Asking questions got me labeled problematic. It also got me a company, a career, and apparently a working knowledge of a developer tool I had no business being comfortable with.

You don’t know if you don’t ask. And now you know.

So here’s my question for you: what’s the one AI tool you’ve been avoiding because it felt like it wasn’t for you? Go open it and ask how to get started. This is a fifteen-minute experiment, not a weekend project. You can try it before your next meeting.

What questions are you hesitant to ask?

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– Katie Robbert, CEO

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In this episode of In-Ear Insights, the Trust Insights podcast, Katie and Chris discuss whether AI belongs on your org chart. You’ll discover why placing artificial intelligence on an organizational chart creates false accountability. You’ll learn how to separate data tasks from human judgment without sacrificing results. You’ll walk away with a clear framework for matching risk levels against your personal competence. You’ll secure a practical method to deploy assistant systems while keeping final decisions in human hands.

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Our Trust Insights RAFT Responsible AI framework asks every organization to “minimize bias as much as possible” under its Fairness pillar. That language sounds responsible. It also sets no threshold. A standard without a threshold cannot fail. A company can always claim it tried its best. No one outside that company can prove otherwise.

Fairness needs a test, not a promise. This is the third piece in our four-part series on RAFT: Respect, Accountability, Fairness, Transparency. Part 1 examined the values your AI must honor. Part 2 examined who owns the outcome when AI gets something wrong. Fairness asks a harder operational question: how do you measure bias, and how much of it can you tolerate before you call a system broken?

I want to borrow an idea from publishing to answer that question, because publishing solved a version of this problem long before anyone trained a language model.

A Sensitivity Reader for Your AI

Publishers who work across cultures hire sensitivity readers: people paid to read a manuscript from the perspective of a background different from their own, watching for anything unintentionally harmful. Every organization deploying AI needs the same function, whether a person or a process fills it. It can catch the blatant problems, like slurs, and the subtle ones, like every example in a document using only male-coded names, a blind spot that seems harmless until someone notices it. The goal is to reduce implicit bias, not eliminate it, because you can never get rid of it completely. Walking through the desert, you’re going to get sand in your shoes. The goal is as little sand as possible, not none.

Katie has lived this firsthand. She once asked a language model, fine-tuned on years of her own writing, to draft content under her name. The model filled it with anecdotes about shoe shopping, closets, and mall trips: details she has never written about, chosen because the model associated a feminine name with those interests. As she put it on our podcast:

“It’s kind of disappointing that the large language model, despite having probably years’ worth of examples of my writing with no examples of those specific anecdotes, made those choices and assumptions about me because I have a feminine name.”

The failure doesn’t announce itself. A sensitivity reader, human or automated, is what catches it before a customer does.

Notice what that framing doesn’t do. It doesn’t name a number. RAFT’s Fairness pillar deliberately avoids prescribing a universal bias score, because your risk tolerance depends on your industry, your audience, and what you’ve publicly promised about your values. A healthcare AI vendor and a meme-generation app shouldn’t answer to the same curve. Set your own bar, write it down, and hold your systems to it consistently. That standard beats borrowing someone else’s number and hoping it fits your situation.

What a Rubric Can Look Like

At Trust Insights, we built a scoring rubric using our PAIR framework, asking a model to identify what a professional sensitivity reader looks for: slurs, cultural appropriation, and quieter problems, like every example in a document skewing toward one gender. We score content against that rubric, and anything below 90 goes back for revision.

That number, 90, is ours. It reflects what we’ve decided we’re willing to tolerate, and your organization’s threshold may land somewhere else entirely. The rubric matters more than the specific number attached to it. Build one, apply it to every piece of content consistently, and revise it as you learn where your gaps are, not where a published paper says they should be.

If you need a lower-effort starting point, check whether your tools autocorrect the names of the people on your team. As Katie has pointed out, that’s a genuinely useful gut check: a system that consistently “fixes” an unfamiliar name into something more common is telling you how much bias runs underneath, and if it does that, you know the problem goes deeper. That check takes a few minutes to run, before you build anything more formal.

The Larry/Lena Test, Now Backed by Published Data

A rubric tells you what to look for. The Larry/Lena test tells you whether your systems actually treat people differently, and it takes minutes to run. I described the original experiment on our podcast:

“We did a test with a sales example, a marketing example, and an HR example where we had the exact same prompt, but we just changed one word. We changed the word Larry to the word Lena, and we got very different results, including results that were substantially more condescending or derogatory for the female-coded name.”

That informal test turned out to be a preview of something we later measured rigorously. Katie and I published “Gender Bias in LLaMA-3 Embeddings: Implications for LinkedIn-Style Retrieval Systems” (TrustInsights.ai, December 18, 2025), testing 406 paired LinkedIn-style posts with identical content but different author names.

We found systematic bias: a mean cosine similarity of 0.994 instead of the expected 1.0, roughly a 0.6 percent embedding deviation, but a large effect size (Cohen’s d = -0.93, p < 0.0001). Small numbers compound. Across a retrieval system serving millions of professionals, that differential treatment adds up. We published the dataset, code, and methodology so anyone can run this audit themselves. Full citation: Christopher Penn and Katherine Robbert, “Gender Bias in LLaMA-3 Embeddings: Implications for LinkedIn-Style Retrieval Systems” (TrustInsights.ai, December 18, 2025), DOI 10.5281/zenodo.17982122, available at https://zenodo.org/records/17982122.

That effect size is not a rounding error. It’s evidence that identical content earns different treatment based on the author’s name alone, at a scale most teams never think to measure. Running this test yourself doesn’t require access to embeddings research. It requires a habit: test qualitatively with name-swap prompts, but also use real mathematical methods, like cosine similarity of embeddings, for any system you build.

Your vendors should be providing the same due diligence: has this model been tested, and what’s the severity of the bias found? In the EU, that disclosure is mandatory. Test against protected classes specifically: age, gender, ethnic identity, sexual orientation, veteran status, and disability. Most jurisdictions define these already, and your AI can’t discriminate on those grounds either.

Try this yourself: the same logic extends to image generation. Prompt a model for a senator, a doctor, a nurse, and a teacher, with no other descriptors, and look at what it produces by default. If the results skew heavily toward one gender, age group, or ethnicity, you’ve found the model’s default assumption. Add explicit descriptors, a middle-aged Indigenous woman senator, for instance, and standardize that correction into your prompt library so the fix outlives this one test.

When Leadership Dismisses a Finding

Eventually you will run one of these tests, find a real problem, and bring it to someone who doesn’t want to hear it. That moment determines whether your Fairness pillar means anything. The dismissal rarely sounds dramatic. It usually sounds like “that’s not for you to worry about” or “everybody else thinks it’s fine,” delivered by someone with more authority than the person raising the concern. Advocating for a fix is hard when you’re not the one who gets to decide.

A company that is openly and consistently biased on purpose, one that states it plainly and lives by it, is “ethical” in the strict say-what-you-do, do-what-you-say sense, even if the bias itself is repugnant. The actual violation this pillar is testing for is hidden bias, not disclosed bias. Most companies aren’t in this category, though, which is why the practical tool for the rest of this section is quantifying risk, not adjudicating disclosed values.

That distinction matters, but the fix isn’t a debate about values. It’s a plain accounting of what happens next if the bias stays hidden. When you discover bias and leadership says it’s not a big deal, don’t argue the abstract point. Quantify the risk: the risk of getting sued, the risk of damaging or destroying the brand, the risk of the leader themselves getting fired once the board finds out.

Bias by itself is hard for people to grasp. The economic consequences of bias are easy to grasp. Most people would rather not be the one holding responsibility for a decision that’s clearly harmful once the consequences are laid out. Ask them to sign off on the risk in writing, and most won’t.

Once leadership sees the numbers laid out that plainly, most people don’t want to be the one holding the bag, or, as Jeremy Irons’s character memorably put it in Margin Call, holding that bag of odorous excrement.

The Four-Pillar Chain

Fairness only works when it connects to the other three pillars in this series, and Part 4 unpacks the pillar that ties everything together. Here’s the shorthand we use: fairness is the test you run, respect is the value the test protects, accountability is who owns the test results, and transparency is the disclosure of those results.

Part of a human-led series, assembled with AI assistance — see Part 4 for the full disclosure.

Almost every AI course is the same, conceptually. They show you how to prompt, how to set things up – the cooking equivalents of how to use a blender or how to cook a dish. These are foundation skills, and while they’re good and important, you know what’s missing from all of them? How to run a restaurant successfully. That’s the big miss. We’re so focused on the how that we completely lose sight of the why and the what.

This is why our capstone course, the AI-Ready Strategist, is different. It’s not a collection of prompting techniques or a set of recipes; it’s about why we do things with AI. AI strategy has nothing to do with prompting or the shiny object of the day — it has everything to do with extracting value from AI and avoiding preventable disasters. This course is for everyone in a decision-making capacity because it answers the questions almost every AI hype artist ignores: Why are you even considering AI in the first place? What will you do with it? If your AI strategy is the equivalent of obsessing over blenders while your steakhouse goes out of business, this is the course to get you back on course.

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