INBOX INSIGHTS: Did I Write This Post, Responsible AI Part 4 (2026-07-29)

INBOX INSIGHTS: Did I Write This Post, Responsible AI Part 4 (2026-07-29) :: View in browser

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AI Disclosures: Did I Write This Post?

Last week I wrote about how you don’t know what AI is capable of unless you ask. I got some really positive feedback on that post, which is always nice to receive.

I also managed to rile up someone who was convinced my team and I were lying. That the post was “AI slop” and that I didn’t write it. They were considering (threatening) ruining my reputation over it. I don’t know what specifically triggered the anger, and hopefully we can all move past it. But I do think it’s a great opportunity to share my writing process and talk about the importance of AI disclosures.

So let’s talk about it.

First, the discourse

There’s a lot of it around the use of AI in writing. A couple of months ago I gave a talk for an organization of writers that focused on the human pushback against AI, how to identify it, and what to do about it. The message wasn’t “don’t use AI.” It was to understand the fear behind using AI with writing.

Recently, Substack released a tool that scans content and scores whether or not it was written by AI. Chris wrote a whole post about this, which you can read here. The bottom line: the Substack tool is not reliable for determining whether or not content was written by AI, especially since most of these detection tools were trained on content written by humans, often without their consent.

Second, the disclosure

AI disclosure is important, and in some places, a requirement. It’s a good best practice to start. And to the person who assumed my content was written by AI (and not me), thank you. I will do a better job of stating my disclosures. Early and often.

Third, the process

It should come as no surprise that I start with the 5P Framework by Trust Insights™ (Purpose, People, Process, Platform, Performance). Usually.

I don’t always have an idea for what to write about that week. Sometimes I ask our Analytics for Marketers community what questions they’re wrestling with. Sometimes I look online, like at Reddit, to see what the discussions are. And sometimes, I use AI. We took a long time to craft our Ideal Customer Profiles (ICPs), synthetic versions of our best-fit customers built from our own data. We have a skill called “ask-the-ICP,” and when I’m stumped, I do just that. Yes, they’re synthetic, and not a true replacement for talking to real customers. But we’re a small company with limited resources, and I don’t always have the luxury of time, so asking our synthetic ICP is a good enough stand-in.

Once I have a topic in mind, I run it through the 5P Framework by Trust Insights™.

Purpose: Why am I writing this in the first place? Is it educational? Is it thought leadership? I like to determine the Purpose of the post instead of just winging it, so it stays focused.

People: Who is this for? This is where the ICP comes in again. We have a few different segments of ideal customers, and not every post can meet the needs of every segment. The old adage “you can’t be everything to everyone” rings true, even in content creation.

Process: How do I write? With my hands. Sometimes. Depending on how far along I am in developing the topic, I create content a few different ways. 1) audio. I learned from Chris that it’s very efficient to record a voice memo and give that to AI to clean up. 2) draft it the old-fashioned way, in a document. Then I give it to AI to clean up. 3) write down a bunch of unconnected thoughts into Cowork and use AI to help me string them together into a coherent story. The bottom line is that it all comes from me and my brain.

Platform: Here’s where the real AI disclosure comes in. I have a few specific tools that I rely on. That I have built. Customized for me.

The first is my writing style skill. This is one of my most important tools. It’s been trained on years of my writing samples, and I’ve started regularly updating it to make sure it evolves as I do. I haven’t done the formal comparison, but I’d say 9 times out of 10 the writing style skill can mimic my writing with minimal edits. This is important to disclose because as AI gets trained deeper and deeper, it will get harder to tell human writing from AI writing. The catch is that the better versions will be wholly trained and maintained by a specific human. In this case, me.

The next tool is the Co-CEO skill. Shocker, that’s me again. It has my writing specifications, but also deeper subject matter expertise. This skill is used to balance out my thinking. It’s me, but without the emotion. It’s a good gut check.

Next, I bring back the ask-the-ICP skill to see if the content I created met the initial Purpose. If not, I try again. I keep running the post through the ICP skill until I feel like I’ve gotten something useful.

Then comes the real editing. I like to use Hemingway for this. It has an AI component, but I’ve never found that part useful.

Lastly, I read the newsletter on camera for our YouTube channel. Zero AI was used. All the stumbles and stutters included.

Performance: How did I do? Once the post goes live in the weekly Inbox Insights newsletter, I have to wait and see. Ann Handley introduced a smart metric: rate of response. Meaning, did you write something engaging enough that someone took the time to respond and let you know? Last week, as mentioned, I got a lot of positive responses. And a negative one. I don’t mind negative feedback. It’s helpful to know when something I created doesn’t land.

The moral of the story

Listen, I don’t have secrets, and the use of AI is something people shouldn’t gatekeep. The more we disclose our usage, the more trust we build with our audience.

So my bottom line is yes, I use AI to support my writing. No, AI doesn’t write for me. That is me, the self-taught typist who uses her thumbs and moves slowly. The writer who cannot spell “definetly” correctly the first time if her life depended on it and is grateful for spellcheck.

We all (should) have resources to help make our writing better. Some people have human editors and copywriters. Some people use synthetic versions of those roles. The important thing is that the writing comes from us, the humans. Our stories, our expertise, our experiences. AI doesn’t have that, even if it’s trained on our writing. The first version of this post was long, rambling, and messy. My writing tools helped me clean it up and make it more coherent. So here’s my disclosure: I wrote this post. I used AI to clean it up and format it. I hope that helps clarify things.

How are you using AI in your writing?

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In this episode of In-Ear Insights, the Trust Insights podcast, Katie and Chris discuss the flaws behind AI detection tools and how creators can protect their reputation while using generative writing assistants. You’ll discover why these detection tools misread human writing and how to stop false accusations from damaging your reputation. You’ll learn simple steps to preserve original drafts and voice recordings as undeniable proof of your authorship. You’ll explore ethical disclosure practices that build trust with your audience while keeping your creative process transparent. You’ll gain confidence in navigating AI ethics so you can create content without fear of unfair judgment.

Watch/listen to this episode of In-Ear Insights here »

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Data Diaries: Interesting Data We Found Every conversation about RAFT’s fourth pillar runs into the same unmarked pothole: the word “transparency” is doing two different jobs, and the framework never separates them. Ask a company to be transparent about its AI use, and it might explain how a model reached a decision. Or it might hand over a record of every system that touched the underlying data. Those are two distinct obligations, not one, and treating them as interchangeable lets a company satisfy one while believing it has covered both. This article treats them separately, on purpose: explainable AI decision-making, and documented data touchpoints. Neither substitutes for the other.

Explainability: Can You Show Your Work?

Transparency in the model itself, or in the software wrapped around it, means being able to say: I can tell how this machine made its decision. If I use an AI tool and don’t have it write a log of its decision-making process, I’ve failed transparency on explainable decision-making. Directing the tool to take notes at every stage, in a format I can actually audit, is what lets me point to exactly how the system reached its answer.

That distinction matters because explainability is a configuration choice, not an accident of the technology. A model can be made to show its reasoning at every step. A company has to direct it to do so, and keep the record somewhere auditable. Skip that step, and the failure belongs to the company, not the tool.

This is also where Part 1’s warning about rubber-stamping agentic workflows becomes concrete. A workflow that hands off task after task with no intermediate log cannot be explained after the fact, no matter how carefully a human reviewed the final output in the moment. Explainability has to be built in before the decision happens, not reconstructed from memory afterward.

The Ingredients Behind the Appliance

Documented data touchpoints are about the ingredients. If the model is the appliance, the data feeding it is the ingredients: what kind of data does a company have, and how biased is it? Everything is biased. The real question is what kind. Is the bias direct, against a protected class, or is it collinear? Collinear bias is one of the biggest problems in classical machine learning. A company might not discriminate against Black people directly. It might discriminate on household income instead. But household income correlates with ethnicity closely enough that discriminating on income means discriminating against a protected class, by definition.

The test for catching this is concrete: take a Black family and a Korean family at the same income level. The decision should come out identical. Take those same two families at different income levels, and the decision should track income, not ethnicity. If it doesn’t, that gap is exactly what this kind of transparency exists to catch. Explainability tells a company how the machine decided. Data-touchpoint documentation tells it what data fed the machine, and whether that diet was quietly discriminatory all along.

Answering that question in the abstract isn’t the same as having it documented. An auditor examining this kind of transparency wants granular detail: every system a piece of data touches, every person with access to it, and how well-protected it stays at each stop along the way. If a new hire logged in tomorrow, would they have immediate access to that data point? A company needs a written trail that answers that, not a confident guess reconstructed from memory when someone finally asks.

A Disclosure Statement Is a Practice, Not a Document

A public AI-use disclosure statement matters, but a single static statement isn’t enough. Disclosing AI use has to be continuous, not a one-time filing that a company writes once and forgets. Continuous disclosure reinforces the copyright claim on the human-originated work a company started with, and it respects a real, non-trivial segment of the audience that doesn’t want purely AI-generated content. Telling people when AI is in use, and how, earns their respect for the honesty, even from people who would rather a company not use AI at all.

That kind of disclosure creates a practical asset, not only an ethical one. A company that documents which source data fed which output builds a chain of evidence: proof that a given piece of content derives from its own material, rather than someone else’s. Enterprise systems like IBM’s WatsonX already build in this level of granularity, keeping a full trail from raw data through processed output, so the record exists if the system is ever subpoenaed. A company that can produce that trail retains the copyright on a fully derivative work, even though a machine helped summarize or structure it. Disclosure protects the audience. It also protects a company’s own claim to what it made.

How This Very Series Was Made

That standard applies here, to this series itself, and not only to the companies it describes. So here is the disclosure, in full, rather than a passing mention.

This series is a human-led podcast and a human-led framework. For this round, I recorded verbal answers to Claude’s questions on video, sitting down and talking through each pillar the way I would in any strategy session, screw-ups, tangents, and all. That’s not a flaw in the process. It’s evidence of it: the reasoning, the analogies, and the positions in these four articles are mine, worked out loud, on the record. Claude then assembled those recorded answers into the finished articles you’re reading, organizing, tightening, and structuring the language, but not inventing the substance. The reasoning is mine. The assembly is machine-done. That’s the disclosure: true human input, machine-assisted assembly, and I’m telling you exactly where that line falls, the same way I’d expect any company to tell you where theirs falls.

The Standard: Operate as if You’re Being Audited Tomorrow

Every pillar in this series needs a standard a reader can actually apply, not only a value to admire. For transparency, Katie set that standard years before we ever wrote it down as RAFT:

“I always operate as if we’re going to be audited tomorrow. I like to have those very clear trails.”

The instrument for meeting that standard already exists. It’s the YAML checklist introduced in Part 2 on Accountability, built from RAFT plus a company’s own values, employee handbook, and terms of service. Use that same checklist to audit a company’s efforts across all four pillars: Respect, Accountability, Fairness, and Transparency. Produce the checklist, show the work behind it, and a company is in good shape.

The standard has teeth because real verification frameworks already back it up. A company bound by ISO 27001, SOC 2, or HIPAA compliance has to hold its AI vendors to those same standards. A vendor that can’t demonstrate equivalent rigor is a gap in the company’s own compliance, not a separate problem to set aside. Playing out the audit scenario now means asking concrete questions before anyone forces the issue. What needs to be documented and disclosed? How visible are those disclosures to partners and customers? Are the underlying contracts current? Answering those questions now costs far less than reconstructing years of AI usage after a regulator or an opposing attorney forces the issue.

When Honesty Itself Becomes the Problem

Part 3 raised a similar edge case for Fairness: a company that is openly and consistently biased is technically meeting the say-what-you-do, do-what-you-say test, however repugnant its values. The same logic applies to Transparency, in an even blunter form.

Bluntly honest but harmful transparency is acceptable only within a narrow frame. A company that discloses harmful values and lives them consistently is meeting the say-what-you-do, do-what-you-say standard, even if the values themselves are repugnant. That doesn’t buy a free pass: customers retain the responsibility to take their business elsewhere, except in genuine monopoly situations where that choice doesn’t really exist. What’s actually unethical, and what this pillar is really guarding against, is claiming good values while doing something else entirely.

Two Threads Worth Pulling Again

Two connections from earlier in this series apply directly here.

Data privacy’s transparency angle is straightforward: a company that says it keeps customer data completely safe, and doesn’t, faces more than an ethics problem. It’s likely exposed legally too.

And the “whose values” question from Part 1 resolves the same way here as it did there. Whose values populate a responsible-AI policy depends on who works at a company and who its customers are. A team doesn’t have to be uniform in its makeup; diversity is a good thing. But a company’s values and its customers’ values need to be in alignment. When they’re not, problems surface eventually.

Closing the Loop: Four Pillars, One Test

Four parts, four pillars, and one test running underneath all of them. Fairness is the test you run. Respect is the value the test protects. Accountability is who owns the test results. Transparency is the disclosure of those results.

Part 1 opened this series with the reason any of it matters now: models have grown capable enough to amplify whatever harm is built into how a company uses them. A cordless drill can only do so much damage in careless hands; a Milwaukee Hole Hawg bores a hole through a car. That capability gap is why “responsible AI” can’t stay a vague, feel-good phrase. It has to mean something specific enough to test, to own, and to disclose. Fairness, Respect, Accountability, and Transparency are how a company makes that phrase mean something: four pillars that only function as one connected system.

“Governance is like therapy and dentistry. Those first catch-up appointments are going to be rough, but once you get into a regular rhythm of maintenance, it gets a lot easier.”

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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