License Human AI Feedback Data from Your Business

You may be able to license human AI feedback data when your business has retained clear, authorized evidence of how people reviewed AI work. Describe the task, the output and the review decision together. A collection of thumbs-up reactions or edited text alone does not establish buyer fit, rights or value.

First establish whether this category fits the proposal

micro1's partnership page explicitly includes human feedback on business AI outputs. That interest is not blanket approval of every AI-generated record or a commitment to buy your review history.

Confirm the category early in writing. Ask the buyer to specify whether it accepts the inputs, generated outputs and human review evidence you propose to license. Do not assume a general operational-data proposal includes all three layers or any new evaluation work.

Describe what already exists in your systems before proposing new work. Saved review histories and a project requiring employees to evaluate new outputs are different transactions. The first concerns rights to an archive; the second also concerns labor, instructions and ongoing delivery.

Garrow can discuss the business description without receiving prompts, outputs or review logs. Buyer fit remains subject to the proposed scope and applicable terms.

Define the judgment your employees actually made

Our recommended internal inventory distinguishes factual correction, rule compliance, completeness, escalation and stylistic preference. Use the categories your company actually applied rather than replacing them with labels that sound more scientific.

For a factual correction, identify the approved evidence supporting it. For a policy decision, identify the applicable rule. For a preference, retain that it was a preference. Do not present a wording change as proof the original answer was objectively wrong.

A hypothetical example is a reviewer replacing an unsupported delivery commitment with language permitted by an internal policy. A useful record would preserve the policy reference and review reason, where sharing is allowed. This is an editorial example, not a reported transaction or a customer event.

Preserve disagreement and uncertainty

Document disagreements. If reviewers differed and nobody adjudicated the question, mark it unresolved. Do not silently treat the latest edit as a gold-standard answer.

Preserve task context and provenance

For internal description, map the prompt category, authorized context, model output, review action and final disposition. Explain which of these your archive retains and which are missing. A buyer should not have to infer an invisible task from an edited paragraph.

Record available system and version information where it affects interpretation. If the tool changed during the period, say so. If the original output was overwritten, describe the archive as final approved text rather than a paired correction dataset.

Keep original feedback distinct from later annotation. A team creating review explanations after the fact may add useful interpretation, but those explanations are new work. Identify who created them and how they were checked.

NIST's AI Risk Management Framework is a voluntary framework addressing trustworthiness throughout AI design, use and evaluation. It is not a certification that your feedback is accurate or a valuation method. Use task-specific review evidence to support your package description.

Check rights in every layer of the example

A review record can include company instructions, employee contributions, third-party inputs and generated output. Ask counsel to assess the proposed license across all those layers, including the terms applicable to the AI service when the record was created.

Do not assume an output belongs in a sale simply because an employee accepted it. The underlying prompt may include customer files your company cannot license. A corrected answer may reproduce a restricted passage from a supplier document.

Inspect sensitive context before preparing any sample. Our recommendation is to exclude credentials, legal advice, health information, children's information and restricted client material from routine pilot consideration. These exclusions are a starting point for review, not a complete statement of applicable law.

Use the documents guide for policy references and the support-data guide for operational inputs. Combining record types requires its own approved scope; a useful link is not a permission grant.

Separate archive licensing from paid review services

If a buyer requests new evaluations, ask for a defined task, reviewer qualifications, instructions and acceptance method. Establish whether staff will work in the buyer's system and what access that requires. Keep this work separate from the license to existing records.

Specify how disagreements are handled and who pays for rework. A broad requirement to provide high-quality feedback can become an unlimited obligation if quality and volume are undefined. Ask for a process the company can understand and meet.

Clarify whether the buyer may retain both rejected and accepted submissions. Define permitted use of reviewer identities and any confidentiality obligations. Ask who owns newly created labels and whether the company can continue using its own reviews internally.

Do not infer that a full-company data advertisement compensates evaluation labor. A proposal should identify each deliverable and its payment condition. Our valuation guide explains why price and obligations belong in the same comparison.

Describe selection and evaluation limits honestly

A review archive may cover only work employees chose to escalate. It may omit routine accepted outputs or failures never logged. Note those limits. Avoid claiming that selected records represent all AI use across the company.

Explain how review labels were generated and whether they changed. If employees followed different instructions, make that visible. A buyer assessing model behavior needs to know what the feedback means, not merely that it exists.

Keep training and evaluation purposes explicit in the contract. Ask whether the buyer will reuse the same examples across purposes and how it will describe resulting performance claims. This is a negotiation recommendation, not an assurance that licensing feedback improves a model.

Keep commercial expectations grounded

Do not treat advertised partnership tiers as proof of earnings for your feedback archive. Request terms for the specific category and approved package, including whether payment covers existing records, new review work or both.

Our role is to assess a possible introduction from your description, identify the buyer and seek your agreement. We do not receive review logs, determine licensing rights or promise an offer. A buyer must confirm the category, use and approved package before record review.

For the wider inventory, read business records to license for AI. For work histories that contain no AI review component, read the workflow guide.

Describe your company and review systems through Garrow's intake.