Q01
Long Text
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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.
Sample questions
A preview of what’s in the template. Every question is editable before you launch.
28 questions · ~4 min
Q02
Multiple Choice
Which best describes your primary development focus?
Q03
Multiple Choice
Which evaluation method did you rely on most in the last 6 months?
Q04
Long Text
How important is fairness in your evaluation decisions?
Q05
Long Text
How concerned are you that unrepresentative samples may have affected your evaluation results in the last 12 months?
Q06
Long Text
If you faced any trade-offs between accuracy, speed, and fairness in recent evaluations, please briefly describe them.
Q07
Long Text
How many years of professional development experience do you have?
Q08
Long Text
Thank you for completing the survey! Your responses are anonymous and will be used in aggregate to improve evaluation practices. We appreciate your time.
Q09
Multiple Choice
Have you been involved in evaluating software, systems, or models in the last 12 months?
Q10
Multiple Choice
In your recent evaluations, did you consider sensitive attributes (e.g., gender, ethnicity, income)?
Q11
Long Text
To what extent do you agree: Our evaluation criteria are applied consistently across different user groups.
Q12
Multiple Choice
Which sampling strategy did you use most often in the last 12 months?
Q13
Long Text
Based on your responses in this survey, what would most improve fairness and representativeness in your evaluations?
Q14
Multiple Choice
What is your current seniority level?
Q15
Long Text
In a typical month, approximately how much of your time is spent on evaluation activities?
Q16
Multiple Choice
Which safeguard was most important when handling sensitive attributes in your evaluations?
Q17
Long Text
To what extent do you agree: I have adequate tools and methods to detect bias in evaluation outcomes.
Q18
Long Text
Rank the following segments by priority for coverage in your evaluations (top = highest priority).
Q19
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.
Q20
Long Text
Which region do you primarily work in?
Q21
Long Text
To what extent do you agree: Stakeholders from diverse backgrounds are involved in designing our evaluations.
Q22
Long Text
Approximately what minimum sample size do you typically need to trust a feature-level evaluation decision?
Q23
Long Text
Approximately how many employees are in your organization?
Q24
Long Text
To what extent do you agree: Fairness considerations sometimes conflict with other priorities such as speed or cost.
Q25
Long Text
How confident are you that your evaluations fairly represent real-world use?
Q26
Long Text
How many people are on the team you primarily work with?
Q27
Long Text
In one or two sentences, how do you define a "fair" evaluation?
Q28
Long Text
What is your primary industry or domain?
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.