# Quality Controls (https://terac.com/docs/researchers/trust-and-quality/quality-controls)

The review methods, AI moderation, and quality signals Terac gives you to keep low-effort and dishonest work out of your dataset.

Verification confirms who a participant is. Quality controls confirm that the work they submitted is genuine and meets your standards. Terac gives you several tools, from fully manual to AI-assisted, so you can match the level of scrutiny to the stakes of your study.

## Choosing a Review Method [#choosing-a-review-method]

| Method            | How it works                                                                                                                               | Best for                                     |
| ----------------- | ------------------------------------------------------------------------------------------------------------------------------------------ | -------------------------------------------- |
| **Manual review** | The submission waits in Work review until you approve or reject it                                                                         | Complex or nuanced work                      |
| **Auto-approve**  | A `completed` redirect from your external tool approves it and pays the participant. On a task without a redirect it waits for you instead | High-volume external surveys                 |
| **Self-report**   | The participant's own completion approves it and pays them, with no review from you                                                        | Steps where the participant's word is enough |

Review method is set per task. See [External Surveys & Redirect URLs](https://terac.com/docs/researchers/integrations/external-surveys) for how each one treats a completion redirect.

See [Reviewing Work](https://terac.com/docs/researchers/results/reviewing-work) for the full workflow.

## Decisions and Signals [#decisions-and-signals]

When you review a submission, you have more than a binary choice. Each action feeds the signals that protect quality across the platform.

* **Approve** the work meets your standards and the participant is paid.
* **Reject** the work does not meet standards; you can include a reason.
* **Strike** flags serious problems such as repeated low effort or dishonesty.
* **Participant ratings** let you score quality after review. Ratings influence a participant's standing and whether they are recommended for your future opportunities.
* **Kick out** removes an unresponsive or spamming participant and frees their slot.

> Because ratings and strikes carry forward, high-quality participants surface
> more often in your future studies and the panel improves over time. Reserve
> rejections and strikes for genuine quality problems.

## AI Moderation and Verification [#ai-moderation-and-verification]

For studies that need deeper assurance than a form submission can provide, the **AI moderator** conducts voice interviews that screen, probe, and verify in real time. Beyond gathering qualitative depth, moderated interviews are well suited to verifying claims and surfacing inconsistencies on longer or higher-stakes tasks, where a static submission alone is easier to game.

You attach a research guide with a screening policy, and the moderator evaluates responses against it. See [Research Guides](https://terac.com/docs/researchers/opportunities/research-guides) and [Interviews](https://terac.com/docs/researchers/results/interviews).

## Attention and Comprehension Checks [#attention-and-comprehension-checks]

Attention and comprehension checks are your first line of defense against low-effort responding inside a task. They work best when they are **fair**: they should test attention to instructions that genuinely matter, not trick attentive participants with ambiguous wording. Pilot your checks alongside the rest of your study, and treat a high failure rate as a sign that the check, not the participant, may be at fault. See [Avoiding Bias](https://terac.com/docs/researchers/research-methods/avoiding-bias).

## A Layered Approach [#a-layered-approach]

No single check is sufficient on its own. The strongest setups combine:

1. Required [attestations](https://terac.com/docs/researchers/trust-and-quality/verified-participants) so applicants are verified.
2. [Filters and screening](https://terac.com/docs/researchers/opportunities/screening) so they fit your population.
3. Fair in-task checks so low-effort work is visible.
4. A [review method](https://terac.com/docs/researchers/results/reviewing-work) matched to your stakes.

> **Warning**
>
> Quality assessment is about converging signals, not one number. Avoid
> rejecting on a single ambiguous indicator. Weigh completion behavior, check
> performance, and content together before acting.

## What's Next? [#whats-next]

- [Reviewing Work](https://terac.com/docs/researchers/results/reviewing-work): The full approval workflow
- [Data Handling](https://terac.com/docs/researchers/trust-and-quality/data-handling): What you receive and your responsibilities