Evaluation Fairness & Representation Perceptions Survey for Developers
Measures software developers' perceptions of fairness, bias, and representativeness in their evaluation practices. Ideal for engineering leadership and DEI teams seeking to identify gaps in evaluation methodology and build more inclusive processes.
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AI-Powered Questions
Intelligent follow-up questions based on responses
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Real-time sentiment and insight detection
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Detailed Reports
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Template Overview
28
Questions
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This professionally designed survey template helps you gather valuable insights with intelligent question flow and automated analysis.
Sample Survey Items
Q1
Chat Message
Welcome! This survey explores your experiences and perspectives on fairness, bias, and representativeness in software and model evaluations.
Your participation is completely voluntary and you may stop at any time. All responses are anonymous and will be reported only in aggregate. There are no right or wrong answers — we are interested in your honest opinions.
Estimated time: 8–10 minutes.
Q2
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
Other (please specify)
Q3
Multiple Choice
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
No
Q4
Dropdown
In a typical month, approximately how much of your time is spent on evaluation activities?
0–10%
11–25%
26–50%
51–75%
76–100%
Prefer not to say
Q5
Multiple Choice
Which 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
Other (please specify)
None/Not applicable
Q6
Multiple Choice
In your recent evaluations, did you consider sensitive attributes (e.g., gender, ethnicity, income)?
Yes
No
Not applicable
Q7
Multiple Choice
Which safeguard was most important when handling sensitive attributes in your evaluations?
IRB/ethics review
Legal/privacy review
Data minimization
Aggregation/anonymization
Differential privacy or noise
Limited access/approvals
Stakeholder consent
Bias detection/remediation
Other (please specify)
Not applicable
Q8
Opinion Scale
How important is fairness in your evaluation decisions?
Range: 1 – 7
Min: Not at all importantMid: NeutralMax: Extremely important
Q9
Opinion Scale
To what extent do you agree: Our evaluation criteria are applied consistently across different user groups.
In one or two sentences, how do you define a "fair" evaluation?
Max chars
Q14
Opinion Scale
How concerned are you that unrepresentative samples may have affected your evaluation results in the last 12 months?
Range: 1 – 7
Min: Not at all concernedMid: NeutralMax: Extremely concerned
Q15
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
Other (please specify)
None/Not applicable
Q16
Ranking
Rank the following segments by priority for coverage in your evaluations (top = highest priority).
Drag to order (top = most important)
New users
Power users
Underrepresented regions/locales
Low-resource devices
Harm-sensitive contexts
Long-tail queries
Q17
Dropdown
Approximately what minimum sample size do you typically need to trust a feature-level evaluation decision?
Under 100
100–499
500–999
1,000–4,999
5,000–9,999
10,000+
I don't have a specific threshold
Prefer not to say
Q18
Opinion Scale
How confident are you that your evaluations fairly represent real-world use?
Range: 1 – 7
Min: Not at all confidentMid: NeutralMax: Extremely confident
Q19
Long Text
If you faced any trade-offs between accuracy, speed, and fairness in recent evaluations, please briefly describe them.
Max chars
Q20
Long Text
Based on your responses in this survey, what would most improve fairness and representativeness in your evaluations?
Max chars
Q21
AI Interview
We'd like to explore your thoughts on fairness and representativeness in evaluations a bit further. Please share your perspective and our AI moderator will ask a couple of follow-up questions.
AI InterviewLength: 2Personality: [Object Object]Mode: Fast
Reference questions: 5
Q22
Dropdown
How many years of professional development experience do you have?
Less than 1
1–3
4–6
7–10
11–15
16+
Prefer not to say
Q23
Multiple Choice
What is your current seniority level?
Student/Intern
Junior/Associate
Mid-level
Senior
Staff/Principal
Manager/Lead
Other
Prefer not to say
Q24
Dropdown
Which region do you primarily work in?
Africa
Asia-Pacific
Europe
Latin America
Middle East
North America
Oceania
Prefer not to say
Q25
Dropdown
Approximately how many employees are in your organization?
1
2–10
11–50
51–200
201–1,000
1,001–10,000
10,001+
Prefer not to say
Q26
Dropdown
How many people are on the team you primarily work with?
1
2–5
6–10
11–20
21–50
51+
Prefer not to say
Q27
Dropdown
What is your primary industry or domain?
Consumer software
Enterprise/B2B
Finance/Fintech
Healthcare
Education
E-commerce
Gaming
Government/Public sector
Research/Academia
Other
Prefer not to say
Q28
Chat Message
Thank you for completing the survey! Your responses are anonymous and will be used in aggregate to improve evaluation practices. We appreciate your time.
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