Q01
Long Text
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Data Labeling QA, Bias & Instruction Clarity Audit
An operational audit survey for data labeling teams, measuring instruction clarity, bias mitigation practices, QA rigor, and workflow bottlenecks over the last 30 days. Designed for labelers, reviewers, and QA leads.
Sample questions
A preview of what’s in the template. Every question is editable before you launch.
25 questions · ~4 min
Q02
Multiple Choice
In the past 30 days, which of the following tasks have you performed? Select all that apply.
Q03
Long Text
Overall, how clear were the task instructions you received in the last 30 days?
Q04
Multiple Choice
Which of the following bias topics are covered in your current labeling guidelines? Select all that apply.
Q05
Long Text
How clear are the acceptance criteria used for reviewing labeled work?
Q06
Long Text
Approximately what percentage of your labeled items were returned for rework in the last 30 days?
Q07
Long Text
If you could make one change to improve clarity, fairness, or quality assurance in your labeling work, what would it be?
Q08
Long Text
What is your primary working region?
Q09
Long Text
Thank you for completing this survey. Your feedback will directly inform improvements to instruction clarity, bias mitigation, and quality assurance processes.
Q10
Long Text
How long have you worked on this labeling program?
Q11
Long Text
In the last 30 days, how often did task instructions change mid-project?
Q12
Long Text
In the last 30 days, how often did you encounter inputs or labels that appeared biased?
Q13
Multiple Choice
Which review approach is used most often on your current program?
Q14
Long Text
From the list below, rank the top causes of rework you observed in the last 30 days, from most common to least common.
Q15
AI Interview
Based on your responses, we'd like to explore a few of your experiences in more depth. An AI moderator will ask you 1–2 follow-up questions about your labeling operations.
Q16
Long Text
What is your primary working language?
Q17
Long Text
If you encountered any unclear or conflicting instructions in the last 30 days, please briefly describe one example. If none, you may skip this question.
Q18
Long Text
When bias is suspected, how clear is the process for escalating the issue?
Q19
Long Text
How useful was the review feedback you received in the last 30 days for improving your labeling accuracy?
Q20
Multiple Choice
Which of the following activities takes the largest share of your typical work week on this program?
Q21
Long Text
How much total experience do you have in data labeling or annotation?
Q22
Long Text
If you encountered a potentially biased input or label recently, please briefly describe the example and how you handled it. If none, you may skip this question.
Q23
Long Text
How timely was the review feedback you received in the last 30 days?
Q24
Multiple Choice
Which of the following tooling issues most slowed your quality or speed in the last 30 days? Select all that apply.
Q25
Long Text
What is your employment type on this program?
What’s included
AI follow-ups
Adaptive probes on open-ended answers that pull out detail a static form would miss.
Attention checks
Built-in safeguards against rushed answers and low-quality respondents.
AI-drafted copy
Wording, ordering, and branching written by the AI — tuned to your research goal.
Auto report
Themes, quotes, and a plain-English summary write themselves once responses come in.
Ready to launch?
Open this template in the editor. Every part is yours to change before the first respondent sees it.