What Do We Mean by “AI-Generated”?

A recent discussion about a data visualization left me wondering: when we call something “AI-generated,” what are we describing, and what are we judging?

I want to separate a few questions that seem to get bundled together, especially the perception of the work and how the work is done. These are meanings I have sensed in some conversations, along with a few thoughts of my own. I would welcome additions or corrections.

1. One label can carry several judgments

Literally, “AI-generated” describes how something was made. It doesn't have to be a criticism. In conversation, though, I sometimes hear other possible connotations:

• Low quality. The work is vague, repetitive, or offers no new information. It may look complete without solving a useful problem.
• A lack of care. Did the author participate seriously, or just pass along the output? Do these words actually express what they think?
• Ungrounded claims. The text or image looks plausible, but was there research, data, or verification behind it? Is it communicating an experience or inventing one?
• An unwelcome style. The rhythm, vocabulary, or structure feels tediously familiar, or unfamiliar in a way that seems unnatural.
• Fairness and wider concerns. Worries about jobs, the value of learned skills, or tools changing the relationship between effort and reward. Training material, creators' rights, and community rules can also raise substantive questions; these concerns cannot all be reduced to emotion.

These are possible meanings I am trying to understand. I don't know a particular commenter's motives.

Care and grounding especially need separating. Someone can research a question seriously and still reach a wrong conclusion. Someone else can use AI to translate an analysis that already has solid evidence. Effort and evidence answer different questions.

If the concern is factual, we need to discuss the facts. If it is about employment, fairness, or the creative process, that deserves a direct conversation, too. A single AI label doesn't tell the other person which question to answer.

2. Perception: make the work understood

One thing I can improve is how my writing comes across. Repetitive sentences, empty introductions, and awkward rhythms can be addressed through better writing assistance, feedback, and editing. Making myself understood is part of communicating. As tools and experience change, people’s familiarity with these styles and their preferences may change, too.

But I want to distinguish a few standards:

• Clarity. Can the reader understand what I mean and identify the actual facts and arguments?
• Fidelity. Has translation or editing changed my meaning, turned uncertainty into confidence, or added a promise I never made?
• Preference. If the rhythm differs from what I am used to, is that enough to judge the author's sincerity or decide that a machine wrote it?

As someone writing in a second language, I am trying to find a language we can share. If AI helps me express a thought I worked through in Chinese, the alternatives sometimes include saying less, being less clear, or never starting the conversation.

There is a real tradeoff. Language assistance can make communication easier while also flattening a person's voice. If unfamiliar phrasing is treated as evidence of a machine, and using a tool to become more fluent is treated as insincere, a non-native speaker can get caught between two demands.

A 2023 study found that the GPT detectors tested frequently mislabeled a sample of human-written English essays by non-native speakers. It studied automated detectors, so it cannot establish how accurately people judge posts by tone. But judgments about authorship need scrutiny, too.¹

I want to improve my writing. I also hope we can ask for clear, specific communication while leaving room for different language backgrounds.

3. How the work is done

If a piece of work is vague, wrong, or useless for making a decision, being made by a human doesn't fix it. Using AI doesn't exempt it from scrutiny, either.

Asking how much AI was used is also insufficient. Rewriting eighty percent of the sentences and inventing the one piece of data supporting a conclusion can have very different consequences. We need to know where AI contributed and what that contribution did. In contexts such as firsthand accounts or skills assessments, the origin and process can also be part of what is being evaluated.

When I started visualizing file classifications, the implementation seemed straightforward: use a Python package to get a prototype on screen, then make it look good. AI is a useful assistant here. It can help debug the code and handle color parameters and plotting syntax I don't want to keep looking up. Familiar ideas become something I can try much sooner.

That help extends beyond drawing a chart. Having AI help run XGBoost, compare models, or try different ways of aggregating features can make exploration much faster. It can help me find information and try methods, giving me new material and experimental results to work with. Deciding how to use those results still takes thought and verification.

I also tried training models and clustering files, with underwhelming results. One problem I encountered was that the classifications drifted and didn't reliably fit the user's situation. Even with the files sorted into groups, I couldn't use those groups to decide what to keep or clean up myself. That made me reconsider the next step: should I keep training models and adding features, or first work out what decision the user actually needs to make?

That is also why I wouldn't measure participation simply by how much code someone wrote by hand. The important question is whether the choices within the workflow have been thought through and checked:

• Data: what counts as an acceptable input? For an analysis of website traffic, should bots be excluded? Are we deduplicating requests, visits, devices, or users? What about the time window and missing data? Successfully running the code doesn't settle the definition of the metric.
• Classification: why these groups? For files, different folders can have different purposes and reasons for accumulating content. I need to understand origins, dependencies, and features before choosing suitable rules or models. Producing four groups doesn't make them meaningful. I also need to check consistency across runs and whether the meaning of a label drifts.
• Presentation and action: what can the user do next? Many tools can draw the chart. The user may need to decide what can be cleaned up, what should be kept, and what requires their own judgment. Color and animation cannot make those decisions for them.
• Acceptance: what would establish that the work is done? Each step needs criteria tied to its purpose. The whole path then needs checking against the outcome the user actually needs.

Even cleanup is hard to reduce to a binary decision:

• Content that cannot readily be recovered, such as journals or personal notes without a confirmed backup, shouldn't become junk just because a classifier labels it that way.
• Content that can be downloaded or restored still requires checking whether the source and version are available, and what recovery would cost.
• Content that can be regenerated needs checks too: would deleting it disrupt current work, and is rebuilding it practical at an acceptable cost?
• Content that needs the user's judgment can be presented with its purpose, size, and consequences. Being deletable doesn't automatically make deletion desirable.

These choices about reversibility need refinement through design and testing. AI can help organize features, compare rules, and implement a solution. I still need to know what the decisions rest on.

Perception and quality affect each other. Clear communication helps people examine the work, and sound work helps build trust. Improving the rhythm of a sentence cannot replace checking the data and the result.

4. Workflow: check the answer against reality

I increasingly think quality depends on the whole workflow. Asking for an outcome and waiting for it to appear leaves out many steps that need verification.

Suppose AI tells me that pausing a customer's subscription is harmless because billing will still happen automatically next month. A quick, complete-sounding explanation doesn't make that a verified rule. At this point, it is still a claim to test.

I need checkpoints:

• Turn the claim into specific questions. What state changes when the subscription is paused? When does it resume? What should happen next billing period, and what supports that expectation?
• Reproduce the operation in an appropriate test environment. Use a test account and inspect the state that was actually saved, beyond a message saying the operation succeeded.
• Check what happens afterward. A successful pause doesn't establish next month's behavior. I need to examine cycle transitions, billing records, or events. What I haven't observed remains unverified.
• Revise the conclusion when the evidence differs. Investigate, adjust, and test again. One passing test account also doesn't establish that every customer state is covered.

These checkpoints help me base a judgment on facts I can observe and examine. That evidence is what I mean by ground truth. Another AI agreeing, or the same AI repeating its reassurance, cannot replace it. I need to be clear about what has and hasn't been checked.

AI can help design the checks, run tests, and interpret results. Tests themselves can miss important cases, so a passing result still needs to correspond to a relevant acceptance criterion.

Breaking the work into steps I can check, designing a way to validate each one, and using the results to revise what comes next is also how I build trust in my own work. I need to know why a judgment holds, under what conditions, and where I am still unsure.

5. Use AI to accelerate research and verification

Sometimes I think of AI as a buffet. Much of what I once had to prepare from scratch is now laid out in front of me. I can keep choosing rich desserts, or put together a balanced meal. More choice and easier access don't guarantee that I end up with what I need. The combination still deserves thought.

The same goes for work: more models, more features, and a prettier chart can all become easy next steps. AI can help me compare those options, too. I need feedback from actual use to decide which are worth pursuing.

I also need to watch for another possibility: an answer feels complete, so I assume it covers the whole problem. I compare only the options it presents and forget there may be others.

The way I want to work includes:

• Using AI to develop more hypotheses and notice questions I hadn't thought to ask.
• Bringing those hypotheses back to data, actual use, and test environments.
• Letting the findings change the next step, instead of circling around the first plausible answer.

I am comfortable with AI participating in that process. It can contribute ideas and carry out work, giving me time to explore more questions. How far the work deserves to be trusted depends substantially on what we ultimately verified, and how much those checks actually cover.

That is the discussion I would like to invite:

• When you say or hear “AI-generated,” which meaning comes to mind first? What is missing from this list?
• In a particular context, do you care most about how the work comes across, its quality, or the way it was made? Why?
• What evidence or practices would make you more willing to trust work made with AI?

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Writing note: This essay draws on my spoken reflections and personal notes. AI assisted with organizing the material, examining the arguments, and drafting both language versions.