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To categorize an AI workflow, record two things first: what the system does—generate, transform, analyze, or recommend/decide—and what it works on—text, images, or both. Then add four tags for human control, consequences, rights and risk, and provenance or disclosure. This two-layer method distinguishes drafting a paragraph from rewriting a supplied draft, creating pixels from a prompt from retouching a photograph, and classifying content from making a recommendation that can affect a person. It also gives editors a consistent record for fact-checking, consent review, labeling, audits, and regulatory decisions.
What is the two-layer framework for categorizing AI use?
The first layer describes the AI function; the second describes the modality. A complete record should therefore say, for example, “transform–text,” “generate–image,” or “analyze–multimodal,” rather than simply calling something “AI-made.” The function tells you what changed or what decision was produced. The modality tells you which evidence, errors, rights and evaluation methods matter.
Four cross-cutting tags make the label operational:
- Human-control level: assistive, co-creative, supervised automation, or autonomous.
- Consequence level: low, material, or high-impact.
- Risk and rights impact: privacy, consent, intellectual-property, discrimination, safety and other foreseeable harms.
- Provenance and disclosure: how completely the model, inputs, edits, checks and labeling are recorded.
This is a taxonomy for describing a workflow, not a claim that one detector can reliably identify every output. SemEval-2024 Task 8 treats human-versus-machine text detection as a binary problem but also studies multiple generators and domains. SemEval-2024 Task 4 separates meme analysis based on text from analysis based on both text and visual material. Those distinctions show why a single “AI” label hides important differences in evidence and evaluation.
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How do I categorize an AI workflow step by step?
Classify the artifact at the point where a model acted, and keep a separate entry for each materially different action. Use this sequence:
- Describe the output action. Did the system create new material, alter supplied material, assess existing material, or rank an option for action?
- Choose the modality. Mark text, image, or multimodal when the model used or produced both. Do not infer modality from the user interface alone.
- Identify source material. Record none, user-supplied, third-party, or sensitive input. A generated article based on a private customer file is not equivalent to one prompted with no personal data.
- Set human control. Use assistive when a person accepts or rejects each suggestion, co-creative when the person and model iterate, supervised automation when a person monitors a process with defined intervention points, and autonomous when the system acts without case-by-case approval.
- Rate consequences. Low means an error is readily reversible and does not affect rights or access. Material means it can cause meaningful cost, reputation or operational harm. High-impact means it can influence safety, employment, education, housing, credit, health, legal status or another significant opportunity.
- Record the tool and version. Include the model or application, version when available, and access date. A later model update can change behavior even when the prompt is identical.
- Log transformations and checks. List prompts, edits, translations, crops, inpainting, upscaling, human review, factual checks, citation checks and rights or consent checks.
- Write the disclosure and retention rule. State what the audience is told and where the provenance record is retained, including who can correct or remove it.
What counts as AI-generated, AI-assisted and AI-transformed?
“AI-generated” should describe substantial new content produced from instructions or data. “AI-assisted” is broader: it includes suggestions that a person evaluates and incorporates, while the person remains responsible for the final result. “AI-transformed” means supplied material was changed; the original source and the changes should remain identifiable in the record.
Generate: creating substantially new content
Generation starts with no required source artifact, or uses data only as a basis for a new result. Examples include drafting a news paragraph from a brief, producing an illustration from a text prompt, synthesizing a voiceover, or creating a new image composition from a description. Record whether the prompt included third-party or sensitive material, because “new” output can still reproduce protected or private content.
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Transformation operates on an input that should be preserved. Text examples include rewriting for tone, translating, shortening, summarizing, correcting grammar and extracting a structured version. Image examples include retouching, inpainting, removing an object, upscaling, colorizing and style transfer. A transformed photograph is not the same category as an image generated from an empty canvas: viewers may need to know which parts remain photographic and which were synthesized.
Analyze: classifying or evaluating existing material
Analysis produces an assessment rather than a replacement artifact. It can classify a document, extract entities, detect a face, compare two images, caption a photograph, identify a language or evaluate whether a passage meets a rule. Analysis can still be high-impact: a classifier used to screen applicants or flag fraud may affect a person even if no text or pixels are generated.
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Recommend or decide: ranking an action
Recommendation systems rank options or propose an action, such as which document to review first or which image is most likely to violate a policy. Reserve decide for a system granted operational authority to carry out the action without a human making the final choice. Record the threshold, escalation path and override capability; a recommendation with automatic execution is operationally closer to an autonomous decision than to an assistive suggestion.
Why must text, image and multimodal uses stay separate?
Text and image workflows expose different evidence and failure modes. Text requires checking factual claims, quotations, citations, translation accuracy and unintended omissions. Image workflows require checking whether an edit is detectable, whether depicted people consented, whether a likeness or source image is licensed, and whether the provenance survives export. Multimodal systems require both sets of checks, plus verification that the text and visual elements agree.
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| Workflow | Typical evidence to verify | Distinct disclosure or rights issue |
|---|---|---|
| Generate–text | Facts, quotations, citations, names, dates and unsupported claims | Tell readers when synthetic text materially shaped the published work |
| Transform–text | Meaning preserved, omissions, translation quality and source attribution | Keep the supplied version and describe substantial rewriting |
| Analyze–text | Class labels, extraction errors, language and domain bias | Explain how a label can affect a person or a downstream decision |
| Generate–image | Visual realism, anatomical or scene errors, prompt bias and provenance | Identify synthetic imagery where viewers could mistake it for a record |
| Transform–image | Which pixels changed, edit boundaries and original-file integrity | Disclose material retouching, compositing or object removal |
| Analyze–image | Detection reliability, false positives, demographic performance and context | Obtain consent for biometric or sensitive analysis and provide review routes |
| Multimodal | Text and visual factual consistency, cross-modal omissions and conflicts | Preserve provenance for every component and explain combined processing |
Which human-control and consequence labels should editors use?
Human control describes the decision process, not a job title. An editor who merely clicks approve after a model output is not exercising the same control as one who checks every claim against primary sources.
- Assistive: the model offers a suggestion and a person decides whether to use it.
- Co-creative: person and model iterate, with authorship and changes distributed across the process.
- Supervised automation: the system handles repeatable cases under monitoring, thresholds and a documented escalation route.
- Autonomous: the system can execute an action without case-by-case human approval.
Pair that label with consequence. A spelling suggestion is usually low consequence. A generated product description may be material if an incorrect safety claim is published. A model that recommends or executes a benefits, hiring or medical action is high-impact even when its output is only a score or ranking.
What governance rules apply to AI text and image workflows?
Use recognized governance frameworks as overlays on the taxonomy rather than as replacements for it. NIST says its AI Risk Management Framework is voluntary and intended to improve the incorporation of trustworthiness considerations in the design, development, use and evaluation of AI systems. NIST released AI RMF 1.0 on January 26, 2023, and its Generative AI Profile on July 26, 2024. Apply its lifecycle questions at design, development, deployment, use and evaluation: what was intended, what data and model were used, how the system was tested, and how incidents are handled.
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UNESCO’s 193 Member States adopted the Recommendation on the Ethics of Artificial Intelligence in November 2021. Its principles include proportionality and do-no-harm, safety and security, privacy and data protection, multi-stakeholder governance, responsibility and accountability, transparency and explainability, human oversight, sustainability, awareness and literacy, and fairness and non-discrimination. UNESCO also states that AI systems should be auditable and traceable and that systems should not displace ultimate human responsibility and accountability.
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The European Commission describes the AI Act as a risk-based framework for specific uses. Its communication aid has four broad levels: unacceptable, high-risk, transparency, and minimal or no risk. High-risk obligations listed by the Commission include risk assessment, data quality, logging, documentation, human oversight, robustness, cybersecurity and accuracy. The Commission states that transparency rules apply in August 2026 and that providers of generative AI must make generated content identifiable, with visible labeling for certain deepfakes and public-interest text. Whether a particular workflow falls into a legal category depends on its use, jurisdiction, provider and deployment date; the taxonomy helps you collect the facts needed for that determination.
What provenance information should accompany each artifact?
Keep a machine-readable or human-readable record attached to the source file, content-management entry or case file. At minimum, fill these fields:
function: generate | transform | analyze | recommend | decide
modality: text | image | multimodal
source material: none | user-supplied | third-party | sensitive
human-control level: assistive | co-creative | supervised automation | autonomous
consequence level: low | material | high-impact
model/tool and version: [record name, version, access date]
material transformations: [prompts, edits, crops, translations, inpainting, etc.]
verification performed: [facts, citations, visual, safety, bias, consistency]
rights/consent review: [status, reviewer, restrictions]
disclosure text: [audience-facing label]
retention/provenance record: [location, owner, retention period]For text, retain the supplied draft when rewriting or summarizing so a reviewer can compare meaning and omissions. For images, retain the original file, edit history and export details when possible. For multimodal work, link the text and image records rather than storing only the final composite.
How can an editor compare two AI workflows?
Compare workflows on the same seven axes before choosing a process or setting a disclosure rule:
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- Source fidelity: how closely must the result preserve supplied words, facts or pixels?
- Human control: where can a person inspect, reject, revise or override the model?
- Factuality or visual realism: what tests reveal invented claims, altered context or convincing visual errors?
- Disclosure burden: would a reasonable audience mistake the result for an unassisted record?
- Rights and harms: could privacy, consent, copyright, discrimination, safety or reputation be affected?
- Auditability: can another reviewer reconstruct the model, inputs, transformations and checks?
- Reversibility: can the original be restored and can an affected person challenge the result?
Do not publish a universal detector-accuracy percentage for text and images. The cited SemEval tasks use different languages, domains, generators and class structures, so one cross-modal number would be misleading. Treat detection as one signal in a documented review, not as proof of authorship or consent.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What are common classification mistakes and how do you fix them?
- Calling every output “generated.” Fix it by marking rewriting, translation, retouching and summarization as transformation when supplied material was changed.
- Ignoring analysis because it produces no prose or pixels. Fix it by recording classifiers, captions, rankings and extracted fields; their consequences can be high.
- Using “human in the loop” without describing control. Fix it with the four control levels and a named approval or override point.
- Dropping modality from the label. Fix it by recording text, image or multimodal and applying the matching factual, visual or cross-modal checks.
- Keeping no original. Fix it by retaining source files, prompts, versions and material edits for a defined period.
- Assuming disclosure alone solves rights issues. Fix it with consent, privacy, licensing, bias and safety review before publication or deployment.
- Treating a recommendation as harmless. Fix it by documenting the action it influences, the threshold, escalation path and person accountable for the outcome.
How does a completed classification look in practice?
A model that rewrites a reporter’s supplied paragraph for clarity is transform–text, with user-supplied source material and usually assistive or co-creative control. The record should include the original paragraph, the final human edits, fact and citation checks, and a disclosure decision based on how substantial the rewrite was.
A system that removes a background person from a photograph is transform–image. Record the original image, the removed content, consent and rights status, the edit tool and version, and a visible label if the change could alter the audience’s understanding of the event.
A tool that ranks applications for a reviewer is recommend–text or recommend–multimodal, depending on its inputs. Because the ranking can affect access to employment or another opportunity, mark it high-impact, require documented human oversight, test for disparate errors, retain logs and provide a way to challenge the result.
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Frequently Asked Questions
Is AI-assisted writing the same as AI-generated writing?
No. Assistance can mean suggestions that a person evaluates, while generation means the system creates substantially new text. The useful record identifies the function, the amount of human revision and whether the source was supplied.
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Do I have to label every AI-edited photo?
The need depends on the edit, audience, jurisdiction and risk. A material change that could make a documentary image misleading warrants clear disclosure; a minor technical correction may call for provenance in the production record instead. Check applicable platform and legal rules, including transparency requirements taking effect under the EU AI Act in August 2026.
Can an AI detector prove that a passage or image was made by AI?
No detector result alone establishes authorship, especially across different languages, domains, generators and image-editing methods. Use detection as one review signal alongside source files, provenance, edit history and human verification.
What should I record when a model analyzes private documents?
Record that the source was sensitive, what data was sent, the model and version, retention and deletion settings, access controls, the analysis purpose, error checks and the person accountable for any resulting action. Add consent or another lawful basis where required.
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Treat it as operationally automated when the system can execute the action or when staff routinely accept its output without meaningful review. Document thresholds, override authority, escalation and appeal routes, then rate the consequence of the action rather than the format of the model’s output.
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