Ethical Use of GenAI: 10 Principles for Technical Communicators

Ethical Use of GenAI: 10 Principles for Technical Communicators

Debra W Kahn (@debrawk.bsky.social)

I was once approached by an extremist organization to desktop-publish some racist content for their upcoming event. I was a new mom running a business on a shoestring budget out of an unused storefront in the same town where I had attended university. Members of the extremist organization had been recently accused of complicity in the murder of a local talk-radio show host in a nearby city.

It was the mid-1980s.

If the political environment sounds all too familiar, so should the ethical situation.

Just as desktop publishing once made it easy to mass-produce messages—ethical or not—GenAI tools today offer unprecedented avenues to content production speed and scale. But the ethical question for content professionals remains: Should we use these tools simply because we can? And if we must use them, how do we use them ethically?

Ultimately, I did not use my skills or my business to propagate the extremists’ propaganda. Nor did I confront them the next day when they returned. On advice from my husband, a member of a minority group in the U.S., I told them I was too busy to turn around their project in the time they requested. This had a kernel of truth to it. I also referred them to a nearby big-box service, whose manager had told me over the phone the night before that she was not empowered to turn away such business (even if she wanted to). Not my most heroic moment.

I am not asking my fellow technical communicators to be especially heroic in the world of GenAI. But I think we should find an ethical stance and stick with it. Using GenAI ethically doesn’t have to be about rejecting the tools; however, it should be about staying alert to risk, avoiding harm, and applying human judgment where it matters most.

In this blog post, I outline the elements of using GenAI ethically and apply ethical principles to real-world scenarios.

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Agent vs Agency in GenAI Adoption: Framing Ethical Governance

Agent vs Agency in GenAI Adoption: Framing Ethical Governance

Debra W Kahn (@debrawk.bsky.social)

Everywhere I look these days, I uncover new terms related to Generative AI (GenAI), some of which have competing definitions. I get lost in the details. My confusion is partly my fault for trying to knit together meaning from too many sources, but it is also due to the evolving nature of GenAI and its application to real-world work environments.

Ay, there’s the rub, as Hamlet would say—GenAI’s nature versus the real world.

Odd isn’t it? To think of GenAI having a “nature” since it is a thing that has been nurtured. Equally perplexing is thinking of the usually ordered world of human work flailing in the face of a single new technology. But that is where we find ourselves these days.

Hamlet’s famous “to be” speech finds him in a moral dilemma, caught between acting—or not—to avenge his father’s death. He contemplates existence versus non-existence and the known world versus the unknown world beyond death, an experience he labels “the undiscovered country.” (Star Trek fans, anyone?) The speech offers a foreshadowing of what is to come in the play.

While not all of us are paralyzed by fear of the unknown, as Hamlet is, many of us struggle with the tensions inherent in the adoption of GenAI by our organizations and content teams. In this blog post, I examine these tensions, share some definitions, and offer suggestions for the ethical governance of GenAI in the content workplace.

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Safeguarding Content Quality Against AI “Slop”

Safeguarding Content Quality Against AI “Slop”

Debra W Kahn (@debrawk.bsky.social)

We are privileged these days to be able to roll our eyes still at fakery created by generative AI. Think of the blurred hands and misaligned clothes in the Princess of Wales’ infamous 2024 Mother’s Day family photo. More recent and brazen examples exist in fake citations included in some lawyers’ depositions and even in the first version of the U.S. government’s 2025 MAHA (Make America Healthy Again) report.

But we likely won’t have that easy eye-roll privilege for long.

The recent iterations of generative AI models, such as ChatGPT 4o, Claude 4, and Google’s Gemini, include even more sophisticated reasoning and huge context windows—thousands of times the size of the original ChatGPT release. Generally, the longer the context window, “the better the model is able to perform,” according to quiq.com.

As I mentioned in my most recent blog post (“Leveling an Editorial Eye on AI”), the omnipresence of AI has the capability—and now the model power—to compound inaccurate information (and misinformation) a thousand-fold, collapsing in on itself. This endangers the whole concept of truth in our modern society, warns my colleague Noz Urbina.

Given this capability, what are reasonable steps an individual, an organization, and the content profession as a whole can take to guard against even the subtlest “AI slop”?

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Leveling an Editorial Eye on AI

Leveling an Editorial Eye on AI

Debra W Kahn (@debrawk.bsky.social)

A colleague and I once pioneered using levels of edits to help manage the workload through our content department at a large high-tech firm. We rolled out the concept and refined it over time, all in the name of efficiency and time to market. What we were really trying to do was save our sanity.

We failed.

Or rather, the whole endeavor of developing and releasing educational content through a single in-house unit failed. All the work—from course design to release—was eventually outsourced. But I learned something valuable from the experience. (And I hope others did, too.)

You can’t outsource quality.

I think that’s as true in today’s world of generative AI as it was “back in the day” when I was a technical editor. But how does editorial refinement work in today’s hungry market for “easy” content? Let’s look at how it used to work, how people would like it to work, and how it might work better.

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Turn Your Keynotes into Content Gold: Writing Tips for Speakers

Turn Your Keynotes into Content Gold: Writing Tips for Speakers

Debra W Kahn (@debrawk.bsky.social)

I recently collaborated on an article with a colleague whose ability to speak extemporaneously impressed me. Not a skill I have ever had. Her delivery was confident, and her knowledge of the subject matter was deep.

But she was intimidated by a blank page.

Even when I drew up an outline for our collaborative article, she seemed to stumble through sentences and lose her way. Who knew?

Professional speakers know how to engage a live audience—but translating that spark into writing? That’s a different craft. The good news? By implementing a few golden strategies, your writing can carry the same power and authority as your voice on stage.

The key is to know what to keep from your speaking style and what to change.

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