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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.

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Data Labeling QA, Bias & Instruction Clarity Audit

Welcome! This survey takes about 11 minutes and asks about your data labeling work over the last 30 days. Your participation is entirely voluntary, and you may stop at any time. There are no right or wrong answers — we are interested in your honest experience. All responses are confidential and will be reported only in aggregate to improve labeling operations. By continuing, you agree to participate.

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テンプレートの内容をプレビューできます。すべての設問は公開前に自由に編集できます。

全23問・約11分
メッセージ

Welcome! This survey takes about 11 minutes and asks about your data labeling work over the last 30 days. Your participation is entirely voluntary, and you may stop at any time. There are no right or wrong answers — we are interested in your honest experience. All responses are confidential and will be reported only in aggregate to improve labeling operations. By continuing, you agree to participate.

Q01
選択式

In the past 30 days, which of the following tasks have you performed? Select all that apply.

  • Labeling / annotation
  • Reviewing / QA
  • Both labeling and reviewing
  • Other (please specify)
Q02
プルダウン

How long have you worked on this labeling program?

  • Less than 1 month
  • 1–3 months
  • 4–6 months
  • 7–12 months
  • 1–2 years
  • More than 2 years
Q03
オピニオンスケール

Overall, how clear were the task instructions you received in the last 30 days?

スケール: 1 – 7
最小:Very unclear最大:Very clear
Q04
オピニオンスケール

In the last 30 days, how often did task instructions change mid-project?

スケール: 1 – 5
最小:Never最大:Very frequently
Q05
自由回答(長文)

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.

Q06
選択式

Which of the following bias topics are covered in your current labeling 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
  • None of the above
  • Other (please specify)
Q07
オピニオンスケール

In the last 30 days, how often did you encounter inputs or labels that appeared biased?

スケール: 1 – 5
最小:Never最大:Very often
Q08
オピニオンスケール

When bias is suspected, how clear is the process for escalating the issue?

スケール: 1 – 7
最小:Not at all clear最大:Extremely clear
Q09
自由回答(長文)

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.

Q10
オピニオンスケール

How clear are the acceptance criteria used for reviewing labeled work?

スケール: 1 – 7
最小:Not at all clear最大:Extremely clear
Q11
選択式

Which review approach is used most often on your current program?

  • Blind double review with adjudication
  • Spot checks (fixed percentage)
  • Heuristic-triggered review (rules-based)
  • Peer review within team
  • Self-review before submit
  • Not sure
  • Other (please specify)
Q12
オピニオンスケール

How useful was the review feedback you received in the last 30 days for improving your labeling accuracy?

スケール: 1 – 7
最小:Not at all useful最大:Extremely useful
Q13
オピニオンスケール

How timely was the review feedback you received in the last 30 days?

スケール: 1 – 7
最小:Not at all timely最大:Extremely timely
Q14
プルダウン

Approximately what percentage of your labeled items were returned for rework in the last 30 days?

  • 0%
  • 1–5%
  • 6–10%
  • 11–20%
  • 21–30%
  • 31–50%
  • More than 50%
  • Not sure
Q15
ランク付け

From the list below, rank the top causes of rework you observed in the last 30 days, from most common to least common.

  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 or context
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Q16
選択式

Which of the following activities takes the largest share of your typical work week on this program?

  • Labeling / annotation
  • Review / QA
  • Guideline reading / updating
  • Meetings / syncs
  • Training / onboarding
  • Escalations or questions
  • Other (please specify)
Q17
選択式

Which of the following tooling issues most slowed your quality or speed in the last 30 days? 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
  • None of the above
  • Other (please specify)
Q18
自由回答(長文)

If you could make one change to improve clarity, fairness, or quality assurance in your labeling work, what would it be?

Q19
AIインタビュー

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.

Q20
プルダウン

What is your primary working region?

  • North America
  • Latin America
  • Europe
  • Middle East
  • Africa
  • South Asia
  • East Asia
  • Southeast Asia
  • Oceania
  • Prefer not to say
Q21
プルダウン

What is your primary working language?

  • English
  • Spanish
  • Portuguese
  • French
  • German
  • Chinese
  • Japanese
  • Korean
  • Hindi
  • Arabic
  • Other (please specify)
  • Prefer not to say
Q22
プルダウン

How much total experience do you have in data labeling or annotation?

  • Less than 6 months
  • 6–12 months
  • 1–2 years
  • 3–5 years
  • 6+ years
Q23
プルダウン

What is your employment type on this program?

  • Full-time
  • Part-time
  • Contract / Freelance
  • Prefer not to say
メッセージ

Thank you for completing this survey. Your feedback will directly inform improvements to instruction clarity, bias mitigation, and quality assurance processes.

含まれる機能

  • AIによる深掘り

    自由回答に合わせてAIが追加で質問し、固定のフォームでは拾えない具体的な内容を引き出します。

  • 注意確認設問

    急いだ回答や質の低い回答者を除外する仕組みを標準で備えています。

  • AIが作成する設問文

    文言、設問の順序、条件分岐をAIが調査の目的に合わせて作成します。

  • 自動レポート

    回答が集まると、テーマ、引用、わかりやすい要約が自動で作成されます。

よくあるご質問

「Data Labeling QA, Bias & Instruction Clarity Audit」テンプレートにはどのような設問が含まれていますか?

すぐに使える設問が23問含まれており、最初の設問は次のとおりです:「In the past 30 days, which of the following tasks have you performed? Select all that apply.」・「How long have you worked on this labeling program?」・「Overall, how clear were the task instructions you received in the last 30 days?」。すべての設問は上でプレビューでき、自由に編集できます。

このアンケートの回答にはどのくらい時間がかかりますか?

回答者は通常、23問を約11分で回答し終えます。

テンプレートは編集できますか?

はい。公開前であれば、すべての設問、選択肢、順序を編集できます。設問の追加や削除のほか、調査の目的に合わせた作り直しをAIエディターに依頼することもできます。

このテンプレートは無料で使えますか?

はい。エディターで開けば、すぐに編集を始められます。お試しにアカウントは不要で、無料プランでアンケートを公開できます。

公開の準備はできましたか?

このテンプレートをエディターで開いてみてください。最初の回答者が目にする前に、すべてを自由に変更できます。

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