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Developer Evaluation Fairness, Bias & Representation Survey

Measure developers' perceptions of fairness, bias, and representation in evaluations. 8-12 minutes, anonymous. Use this template to boost trust and inclusion.

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
Multiple Choice
Which best describes your primary development focus?
  • Frontend
  • Backend
  • ML/AI
  • Data engineering/MLOps
  • Mobile
  • DevOps/SRE
  • Security
  • Full-stack
  • QA/Test automation
Q2
Dropdown
Have you been involved in evaluating software, systems, or models in the last 12 months?
  • Yes, in the last 6 months
  • Yes, 6–12 months ago
  • Yes, over a year ago
  • Not yet
Q3
Multiple Choice
Which one evaluation method did you rely on most in the last 6 months?
  • Unit tests/assertions
  • Offline benchmarks
  • Human ratings/annotation
  • A/B or canary releases
  • Synthetic data tests
  • Red-teaming/adversarial testing
  • Bias/fairness audits
  • None/Not applicable
Q4
Long Text
Briefly describe any trade-offs you faced between accuracy, speed, and fairness in your recent evaluations (last 6 months).
Max 600 chars
Q5
Multiple Choice
In your recent evaluations, did you consider sensitive attributes (e.g., gender, ethnicity, income)?
  • Yes
  • No
  • Not applicable
Q6
Multiple Choice
If applicable, which safeguard was most important when handling sensitive attributes?
  • IRB/ethics review
  • Legal/privacy review
  • Data minimization
  • Aggregation/anonymization
  • Differential privacy or noise
  • Limited access/approvals
  • Stakeholder consent
  • Bias detection/remediation
  • Not applicable
Q7
Long Text
In one or two sentences, how do you define a “fair” evaluation?
Max 600 chars
Q8
Rating
How important is fairness in your evaluation decisions?
Scale: 10 (star)
Min: Not importantMax: Extremely important
Q9
Matrix
Please rate your agreement with the following statements about recent or typical evaluations.
RowsStrongly disagreeDisagreeNeutralAgreeStrongly agree
Sampling reflects target users•••••
Edge cases are adequately represented•••••
Annotators/reviewers are diverse•••••
Metrics align with product goals•••••
Rater instructions are clear and unbiased•••••
Results are reproducible across runs•••••
Q10
Opinion Scale
How concerned are you about unrepresentative samples affecting results in the last 12 months?
Range: 1 – 10
Min: Not concernedMid: Moderately concernedMax: Very concerned
Q11
Multiple Choice
Which sampling strategy did you use most often in the last 12 months?
  • Random sampling
  • Stratified sampling
  • User segment quotas
  • Synthetic augmentation
  • Convenience/availability sampling
  • Production traffic replay
  • Telemetry-driven sampling
  • None/Not applicable
Q12
Ranking
Rank the following segments by priority for coverage in evaluations (top = highest priority).
Drag to order (top = most important)
  1. New users
  2. Power users
  3. Underrepresented regions/locales
  4. Low-resource devices
  5. Harm-sensitive contexts
  6. Long-tail queries
Q13
Numeric
Approximately what minimum sample size do you need to trust a feature-level decision? (enter a number)
Accepts a numeric value
Whole numbers only
Q14
Rating
How confident are you that your evaluations fairly represent real-world use?
Scale: 10 (star)
Min: Not at all confidentMax: Very confident
Q15
Dropdown
Years of professional development experience
  • Less than 1
  • 1–3
  • 4–6
  • 7–10
  • 11–15
  • 16+
  • Prefer not to say
Q16
Multiple Choice
What is your current seniority level?
  • Student/Intern
  • Junior/Associate
  • Mid-level
  • Senior
  • Staff/Principal
  • Manager/Lead
  • Other
  • Prefer not to say
Q17
Dropdown
Which region do you primarily work in?
  • Africa
  • Asia-Pacific
  • Europe
  • Latin America
  • Middle East
  • North America
  • Oceania
  • Prefer not to say
Q18
Dropdown
Organization size (approximate number of employees)
  • 1
  • 2–10
  • 11–50
  • 51–200
  • 201–1,000
  • 1,001–10,000
  • 10,001+
  • Prefer not to say
Q19
Dropdown
Team size you primarily work with
  • 1
  • 2–5
  • 6–10
  • 11–20
  • 21–50
  • 51+
  • Prefer not to say
Q20
Dropdown
Primary industry/domain
  • Consumer software
  • Enterprise/B2B
  • Finance/Fintech
  • Healthcare
  • Education
  • E-commerce
  • Gaming
  • Government/Public sector
  • Research/Academia
  • Other
Q21
Dropdown
In a typical month, how much of your time is spent on evaluation activities?
  • 0–10%
  • 11–25%
  • 26–50%
  • 51–75%
  • 76–100%
  • Prefer not to say
Q22
Multiple Choice
Attention check: Please select “I am paying attention.”
  • I am paying attention
  • I am not paying attention
Q23
Long Text
What would most improve fairness and representativeness in your evaluations over the next quarter?
Max 600 chars
Q24
AI Interview
AI Interview: 2 Follow-up Questions on Fairness and Representativeness
AI InterviewLength: 2Personality: Expert InterviewerMode: Fast
Q25
Chat Message
Thank you for completing the survey! Your input helps us improve evaluation practices.

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