Ethics when you create with AI

▲CONCEPT

Creating with AI opens ethics questions that the verify habit alone does not cover, because now you are making content, not only reading it. The same model that writes you a fair summary of a real speech can write a confident, detailed speech that was never given at all. Same fluency, same polish — only the prompt changes.

That is what makes a deepfake possible: a generated image or video of a real person doing or saying something they never did, made convincingly enough to pass as real.

The test for whether this is wrong is not how good it looks. The test is consent. Making a realistic depiction of a real person without asking them first is harmful, however good the result looks.

One model can produce the helpful version and the harmful one with exactly the same fluency. The risk sits inside the mechanism itself, not only in the hands of a few bad actors.

same capability, two uses

Helpful: summarise a real speechsame model, same fluency — writes an accurate summary of what was actually said

→

only the prompt differs

Harmful: fabricate a speechsame model, same fluency — writes a confident, attributed speech that never happened

the ethical risk is structural, not just misuse by bad actors — the mechanism itself enables fabrication at scale

Ethical dimension

ai-genai-ethics.ethics-synthetic-media

Ethical dimension

Synthetic-media misuse

The same generative model that makes harmless art can make a deepfake — a realistic image or video of a real person doing or saying something they never did. The helpful output and the harmful one come from the same mechanism. Only the prompt changes. That is why the risk is built into the capability itself, not added by a handful of bad actors.

Picture a convincing video of a real teacher appearing to say something they never said, shared online as if genuine. It is harmful no matter how good the video looks, because the test that matters is consent, not quality.

How do you keep the honest uses of generative media, and still stop the non-consensual fakes, when the tool itself cannot tell a harmless prompt from a harmful one?

"Synthetic-media harm only comes from a few bad actors deliberately misusing the tools."

The same model that writes a helpful summary can fabricate a convincing fake speech — only the prompt differs. The capacity to fabricate at scale, with the same fluency as an honest answer, is built into how generation works. That makes the risk structural, not personal. It needs no villain behind it, only the ordinary availability of a tool that cannot tell a helpful prompt from a harmful one on its own.

▲CONCEPT

Here is a mistake worth admitting. I once thought disclosure only mattered when the AI-made result looked obviously fake — a rough image, a stiff sentence. Get a genuinely good result, I assumed, and the question quietly goes away. It does not.

Disclosure means clearly stating when a piece of text, image, audio, or video was made with AI help. It is required in three situations. When AI generated or substantially drafted the work. When the content depicts a real person. And when the content is presented as factual or documentary — where a reader would reasonably assume no AI was involved.

Skip disclosure when one of these applies, and you have deceived your reader, even if nothing in the content itself is false. Disclosure is a question of honesty, not of quality. An AI draft that reads exactly like something you would have written yourself still needs to be said out loud.

disclose AI use in three cases

DRAFTED BY AI — AI generated or substantially drafted the work

A REAL PERSON IS SHOWN — AI-generated content depicts a real person

PRESENTED AS FACT — the content is offered as factual or documentary

ANY ONE →

disclosure required

failing to disclose when required is deception even if the content is true — disclosure is about honesty, not quality

"If the AI-generated content is good quality, disclosure is unnecessary — what matters is the result, not the process."

Disclosure is about honesty, not about quality. A convincing image of a real person doing something they never did is more harmful the more convincing it looks, not less. The quality of the fake changes nothing about the consent question, or the deception question underneath it. The same rule holds for an AI draft that reads exactly like something you would have written yourself. If AI substantially made it, you say so.

▲CONCEPT

One more question follows generative AI everywhere it creates: whose work made this possible? LLMs and image generators are trained on copyrighted text, images, and code, often without asking the people who made them. When a tool produces an image in a living artist's style, that artist's work shaped the result, and the artist gets no credit and no payment for it.

Three questions remain genuinely open, with no settled answer anywhere in the world as of now. Does training on copyrighted work break copyright law? Who owns the output it produces — the user who typed the prompt, the company that built the tool, or the people whose work trained the model? And does generating in someone's exact style violate their rights?

Courts and lawmakers everywhere are deciding these questions right now — there is no verdict yet to memorise.

Ethical dimension

ai-genai-ethics.ethics-copyright

Ethical dimension

Intellectual property & copyright

A generative model is trained on work made by real writers, artists, and musicians, often without their consent. When it produces output in someone's style, that person's work shaped the result, yet they receive no credit and no payment. The law has not yet settled who, if anyone, owns what the model produces.

An image tool generates a picture in the style of a living illustrator whose work sat inside its training data. The illustrator's years of labour shaped the output. Their name appears nowhere near it.

Does training on copyrighted work require the owner's permission? And who owns the result it produces — the user who typed the prompt, the company that built the tool, or the original creators whose work the model learned from?