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Data Labeling QA, Bias & Workflow Survey Template

Audit data labeling operations fast with this survey template. Measure instruction clarity, bias mitigation, QA rigor, and workflow. Easy to customize; private.

What's Included

AI-Powered Questions

Intelligent follow-up questions based on responses

Automated Analysis

Real-time sentiment and insight detection

Smart Distribution

Target the right audience automatically

Detailed Reports

Comprehensive insights and recommendations

Sample Survey Items

Q1
Chat Message
Welcome! This short survey (about 5–7 minutes) asks about your labeling work in the last 30 days. Please answer based on your experience; there are no right or wrong answers.
Q2
Multiple Choice
In the past 30 days, which tasks have you performed? Select all that apply.
  • Labeling/annotation
  • Reviewing/QA
Q3
Dropdown
How long have you worked on this labeling program?
  • <1 month
  • 1–3 months
  • 4–6 months
  • 7–12 months
  • 1–2 years
  • 2+ years
Q4
Opinion Scale
Overall, how clear were task instructions in the last 30 days?
Range: 1 10
Min: Very unclearMid: NeutralMax: Very clear
Q5
Long Text
Briefly describe one unclear or conflicting instruction you encountered.
Max 600 chars
Q6
Rating
In the last 30 days, how often did instructions change mid-project?
Scale: 11 (star)
Min: NeverMax: Very often
Q7
Multiple Choice
Which bias topics are covered in your current guidelines? Select all that apply.
  • Demographic bias (e.g., gender, race, age)
  • Domain or jargon bias
  • Geographic/vernacular variation
  • Label leakage or proxy signals
  • Harmful stereotypes and toxicity
  • Context/translation bias
Q8
Opinion Scale
In the last 30 days, how often did you encounter biased inputs or labels?
Range: 1 10
Min: NeverMid: SometimesMax: Very often
Q9
Long Text
Share one recent example of potential bias and how you handled it.
Max 600 chars
Q10
Opinion Scale
When bias is suspected, how clear is the escalation path?
Range: 1 10
Min: Not clearMid: ModerateMax: Very clear
Q11
Rating
How clear are the acceptance criteria used for reviewing work?
Scale: 11 (star)
Min: Not clearMax: Very clear
Q12
Multiple Choice
Which review approach is used most often?
  • Blind double review with adjudication
  • Spot checks (fixed percentage)
  • Heuristic-triggered review (rules-based)
  • Peer review within team
  • Self-review before submit
  • Not sure
Q13
Matrix
Please rate the review feedback you received in the last 30 days.
RowsStrongly disagreeDisagreeNeutralAgreeStrongly agree
Feedback was timely
Feedback was specific
Feedback was actionable
Tone was respectful
Q14
Opinion Scale
Attention check: To confirm attention, please select Neutral for this item.
Range: 1 10
Min: Strongly disagreeMid: NeutralMax: Strongly agree
Q15
Numeric
Approximate percent (%) of items returned for rework in the last 30 days.
Accepts a numeric value
Whole numbers only
Q16
Ranking
Rank the top causes of rework you observed (most to least).
Drag to order (top = most important)
  1. Unclear or changing guidelines
  2. Reviewer–labeler disagreement
  3. Edge cases not covered
  4. Tooling or platform issues
  5. Time pressure or quotas
  6. Insufficient training/context
Q17
Constant Sum
Distribute 100 points across your typical weekly time on this program.
Total must equal 100
  • Labeling/annotation
  • Review/QA
  • Guideline reading/updating
  • Meetings/sync
  • Training/onboarding
  • Escalations or questions
  • Other
Min per option: 0Whole numbers only
Q18
Multiple Choice
Which tooling issues most slowed quality or speed recently? Select all that apply.
  • Slow loading or lag
  • Limited shortcuts or templates
  • Poor diff/compare views
  • Unclear error messages
  • Hard to flag bias or edge cases
  • Limited audit trail/metadata
Q19
Short Text
What single change would most improve clarity, fairness, or QA?
Max 100 chars
Q20
Dropdown
What is your primary working region? (optional)
  • North America
  • Latin America
  • Europe
  • Middle East
  • Africa
  • South Asia
  • East Asia
  • Southeast Asia
  • Oceania
Q21
Dropdown
What is your primary working language? (optional)
  • English
  • Spanish
  • Portuguese
  • French
  • German
  • Chinese
  • Japanese
  • Korean
  • Hindi
  • Arabic
Q22
Dropdown
Total experience in data labeling/annotation
  • <6 months
  • 6–12 months
  • 1–2 years
  • 3–5 years
  • 6+ years
Q23
Dropdown
Employment type on this program
  • Full-time
  • Part-time
  • Contract/Freelance
  • Not sure/Prefer not to say
Q24
Long Text
Anything else you’d like us to know about clarity, bias, or QA?
Max 600 chars
Q25
AI Interview
AI Interview: 2 Follow-up Questions on labeling operations
AI InterviewLength: 2Personality: [Object Object]Mode: Fast
Q26
Chat Message
Thank you for your time—your feedback helps improve clarity, fairness, and quality.

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