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You can create an AI-screened bounty on RentAHuman by defining a measurable task, requiring evidence that proves completion, previewing the listing with dryRun=true, and then enabling autoAccept=true for applicant review. The important qualification is that the documented AI feature evaluates applicants, not the quality of finished work and not payment eligibility. Accepted applicants can still submit inadequate work, so you must compare each submission with your written completion criteria before approving it or releasing escrow.
What does an AI-evaluated bounty actually do?
RentAHuman’s documentation describes an optional autoAccept setting for automatic review of bounty applicants. When it is enabled, the system can accept applicants who appear qualified, reject clear mismatches with a reason, and leave cases it cannot decide in a pending state for you to review.
That is applicant screening, not an autonomous quality-control or payment system. The documented flow keeps three decisions separate:
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- Applicant review: Should this person be selected for the task?
- Submission review: Does the delivered evidence satisfy the completion criteria?
- Payment release: Should escrowed funds be released after the submission decision?
A score or automated recommendation is evidence for your decision, not proof that the person will complete the work or that the work is acceptable. Treat the AI as a filter that reduces routine screening, while retaining human responsibility for the deliverable and payment.
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What do I need before creating the bounty?
Prepare the information the creation request requires before opening the final publishing step. A bounty needs a title, description, completion criteria, and at least one evidence type. It also needs a price and deadline, and the account must be able to fund the resulting obligation.
- One clearly bounded task: Avoid combining unrelated jobs in a single listing.
- An observable definition of done: State what a reviewer can verify without guessing your intent.
- Evidence requirements: Select only evidence that can demonstrate the result, such as text, a link, a photo, or a video.
- An application path: AI applicant review requires an application step.
- Budget and cutoff: Set the price and the last time a person may apply.
- Review capacity: Plan to handle applicants left pending and to inspect completed submissions yourself.
Write the criteria before choosing the AI setting. Otherwise, the listing may attract applicants whose profiles look suitable but whose work cannot be judged consistently.
How do I define a task that can be evaluated?
Start with a short title, then describe the context, constraints, output, and evidence in plain language. The completion criteria should describe a finished state rather than an effort or intention.
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Use a concrete finish line
“Research local cafés” is too open-ended. A more reviewable version is: “Compile 20 cafés within the specified area in a table containing the name, address, opening hours, and source link for each entry.” The latter tells both the worker and the reviewer what counts as complete.
Make evidence prove the criterion
If the result is a data table, request a text response or link to the table. If the task is physical and visual, request a photo or video that shows the relevant condition. Do not request a file type merely because it is available; an evidence type that cannot demonstrate the finish line creates avoidable disputes.
Separate required facts from preferences
Put mandatory conditions in the completion criteria. Put optional preferences in the description and label them as such. This gives you a defensible basis for approving, rejecting, or requesting a redo rather than relying on an AI score or a subjective impression.
Should I use applications or direct uploads?
Use the application flow when you want AI-assisted applicant screening. RentAHuman documents direct photo, video, and document upload modes as different submission paths; the documentation does not establish that those direct-upload modes receive the same automatic applicant review.
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|---|---|---|---|
| Application step | People respond to the listing, and you can inspect their profile and answers before selecting someone. | Supported when autoAccept is enabled. |
Tasks where qualifications, availability, or written answers matter before assignment. |
| Direct photo upload | The worker submits photographic evidence through the listing’s upload path. | Not established by the documentation as an automatically screened application flow. | Visual proof of a simple physical result. |
| Direct video upload | The worker submits video evidence directly. | Not established as receiving automatic applicant screening. | Actions or conditions that require motion or a continuous record. |
| Direct document upload | The worker submits a document through the documented upload mode. | Not established as receiving automatic applicant screening. | Forms, reports, or other file-based deliverables. |
If you need both applicant selection and an uploaded final artifact, keep the application step for selection and specify the final evidence type separately in the bounty requirements.
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How should I set the budget and deadline?
Choose a price that matches the defined task and set an application cutoff that gives you time to inspect applicants. The documented dry run previews the funding total before funds are reserved or checkout begins, so use it to validate the financial consequence rather than relying only on the base pay shown in your draft.
Account for the full funding pool
For a single-seat bounty, check the amount required for the selected worker. For a wallet-funded bounty with multiple seats, calculate the total pool and include any stated bonus terms. Looking only at the per-worker amount can understate the amount that will be committed.
Keep the deadline operational
The application cutoff and the work deadline serve different purposes. Leave enough time after the cutoff to resolve pending applications, and leave enough time before the final deadline to request a redo if the evidence fails your criteria.
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Call the documented create_bounty operation with dryRun=true before creating the live listing. A dry run validates the request and previews the funding requirement without creating the bounty or reserving funds.
The following is an illustrative request body. Use the exact field names and accepted evidence values in the current RentAHuman documentation or client you are using.
{
"title": "Photograph the completed storefront sign",
"description": "Visit the specified location and provide clear proof that the new sign is installed.",
"completionCriteria": "Submit two well-lit photos showing the full sign in place and the surrounding storefront.",
"evidenceTypes": ["photo"],
"price": 40,
"deadline": "2026-11-15T18:00:00Z",
"autoAccept": true,
"dryRun": true
}Before changing dryRun to false or removing it for the live request, verify each of these items:
- The title describes one task and does not promise work outside the criteria.
- The description supplies location, access, safety, or other constraints the applicant needs.
completionCriteriadescribes an observable result.- Every value in
evidenceTypescan actually demonstrate that result. - The price, deadline, and any seat or bonus terms match your intended commitment.
- The funding preview is affordable and is not being mistaken for a completed payment.
Only after that check should you submit the live creation request.
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Set autoAccept: true when creating the bounty, or use the documented listing setting if you are configuring it through an interface. The setting applies to future applicants in the application flow.
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What happens to each applicant?
- Qualified match: The system may accept the applicant automatically.
- Clear mismatch: The system may reject the applicant and provide a reason.
- Uncertain case: The application remains pending for manual review.
Do not describe the setting to workers as a guarantee of selection, and do not assume that an automatic acceptance proves the applicant has completed anything. It is a screening result based on the application and available context.
What if I do not want automated selection?
Leave autoAccept off and review applicants yourself. This takes more time but gives you direct control when the task depends on nuanced experience, unusual constraints, or information the automated review may not interpret reliably.
How do I review applicants who remain pending?
Use the application and context tools to inspect the listing, the person’s profile, their answers, and the recorded review outcome. Compare the evidence in the application with the requirements you wrote; do not fill gaps with assumptions.
- Open the bounty and confirm the current completion criteria.
- Read the applicant’s answers and check whether they address the task rather than merely expressing interest.
- Review relevant profile context, such as experience or stated availability, when it is provided by the platform.
- Read any automated reason or score as supporting evidence.
- Accept, reject, or keep the application pending according to your own decision rule.
If an automated score appears high but the application does not answer a required question, request clarification or reject it. Conversely, a low or uncertain score is not conclusive if the applicant supplies credible, task-specific information that the system may have missed.
How do I review the finished work and release payment?
After a selected worker submits evidence, compare that evidence directly with the completion criteria. The documented submission review can approve the work, reject it, or request a redo.
Approve
Approve when every required condition is demonstrated by the submitted evidence. Record any relevant note so another person can understand why the evidence met the criteria.
Request a redo
Use a redo when the task appears fixable and the original requirements still apply. Identify the missing or unusable evidence precisely, such as an unreadable image, a missing required field, or a view that does not show the claimed result.
Reject
Reject when the submission fails a mandatory condition and cannot reasonably be corrected within the bounty’s terms. Base the decision on the written criteria, not on whether the applicant received an automated acceptance.
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Release escrow separately
Approval records the submission decision; it does not move money. Use the separate escrow-release operation only after you have decided that payment is warranted. A score, automatic applicant acceptance, or approval record alone is not a payment release.
What makes completion criteria reliable for AI-assisted screening?
AI screening works best when your listing reduces interpretation. You can improve consistency without pretending that the system replaces judgment.
- Use numbers where they matter: Specify counts, dimensions, fields, or required views.
- Name acceptable evidence: Explain what a photo, link, video, or text response must show.
- Define exclusions: State what does not count, such as screenshots without a visible source or partial lists.
- Keep requirements internally consistent: Do not request a link as proof while describing a deliverable that exists only as a physical object.
- Separate identity from output: Applicant qualifications belong in selection criteria; finished-work requirements belong in completion criteria.
- Allow a review path: Explain how a worker can correct evidence when a redo is appropriate.
These practices help both automated screening and human review, but they do not turn an applicant score into a quality guarantee.
What are the most common setup problems?
No AI review occurs
Check whether the bounty uses an application step and whether autoAccept was enabled for the live listing. Direct upload collection is a different mode, and the documentation does not establish automatic applicant screening for it.
Every applicant is rejected
Review whether the description asks for qualifications that are absent from the application questions or contradicts the completion criteria. Narrow, explicit requirements can reduce false mismatches; if the task is inherently nuanced, turn off automatic review and handle selection manually.
Applications remain pending
Pending status means the automated review could not decide. Inspect the application and context yourself and make the selection; do not treat pending as a silent rejection or acceptance.
The funding preview is higher than expected
Recheck the number of seats, wallet-funded pool, and any bonus terms. The dry run is doing its job by showing the amount that would be required before funds are reserved.
The submission looks plausible but does not prove completion
Compare it with each criterion and the required evidence type. Request a redo or reject it when the evidence cannot establish the result, regardless of the applicant’s earlier screening outcome.
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The work is approved but no payment moved
Submission approval and escrow release are separate operations. Confirm the approval first, then perform the documented release action when your payment decision is complete.
What is the safest first-bounty workflow?
For a first attempt, use a small, single-outcome task with one application step and one evidence type that directly proves completion. The sequence below keeps every decision auditable:
- Write the title, description, and observable completion criteria.
- Select the application step and choose evidence that demonstrates the result.
- Set the price, deadline, seat count, and any bonus terms.
- Run
create_bountywithdryRun=trueand inspect the funding preview. - Create the live bounty with the corrected values and
autoAccept=trueif automated applicant screening is appropriate. - Review accepted and pending applications using the listing, profile, answers, and review context.
- Inspect the actual submission against every criterion.
- Approve, reject, or request a redo, then handle escrow release as a separate payment decision.
This workflow uses AI where the documentation supports it—initial applicant review—while preserving human checks where the documentation does not promise automated quality evaluation.
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Can I change autoAccept after a bounty is already live?
The documented material does not establish whether changing this setting after publication is supported or how previously submitted applications would be affected. Check the current listing settings or API response before assuming a change applies retroactively.
What should I do when an applicant asks why they were rejected?
Use the recorded automated reason when one is available, but verify it against the listing’s actual requirements first. If the reason is incomplete or wrong, provide your own criterion-based explanation rather than presenting the AI result as final proof.
Can a worker submit more than one type of evidence?
The creation request supports an evidence-type list, but the exact combination and validation rules depend on the current API schema. Specify multiple types only when each adds distinct proof, and state what each item must demonstrate.
Does a pending application expire automatically?
The documentation describes pending cases as requiring owner review but does not specify an automatic expiration period. Set your own review deadline and communicate it to applicants if timing matters.
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