Measures how ML/AI practitioners engage with model cards, evaluate documented limitations, and how documentation quality shapes trust and adoption decisions across deployment contexts.
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Template Overview
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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 experience with model cards and how model limitations influence your workflow.
Your participation is voluntary — you may stop at any time. There are no right or wrong answers; we are interested in your honest opinions. All responses are anonymous and will be reported in aggregate only.
Estimated time: 8–10 minutes.
Q2
Multiple Choice
In the past 6 months, how have you worked with ML models? Select all that apply.
Implemented or fine-tuned models in code
Consumed prebuilt APIs/SDKs
Evaluated model performance for a project
Selected vendors or models for deployment
Wrote or maintained documentation
None of the above
Q3
Multiple Choice
How familiar are you with model cards?
Very familiar — I regularly read and apply them
Somewhat familiar — I've read a few
I've heard of model cards but I'm not sure what they include
Not familiar — I've never heard of them
Q4
Multiple Choice
Based on what you currently know, which information would you expect to find in a model card? Select all that apply.
Intended use and out-of-scope uses
Training data sources and collection methods
Evaluation metrics and methodology
Performance across subgroups or conditions
Known limitations and failure modes
Safety/ethics considerations
Versioning and change history
Licensing and usage terms
Contact/support information
Deployment requirements and constraints
I don't know / not sure
Other (please specify)
Q5
Chat Message
Quick primer: A model card is a concise report that outlines a model's intended and out-of-scope uses, data provenance, evaluation methods and results (often across subgroups), known limitations and failure modes, and relevant safety/ethical notes. It helps you judge fit and risks before integrating a model.
Please keep this definition in mind for the remaining questions.
Q6
Opinion Scale
If a model card is available, how likely are you to read it before using the model?
Range: 1 – 7
Min: Very unlikelyMid: NeutralMax: Very likely
Q7
Multiple Choice
From the model cards you've reviewed, which elements were commonly included? Select all that apply.
Intended use and out-of-scope uses
Training data sources and collection methods
Evaluation metrics and methodology
Performance across subgroups or conditions
Known limitations and failure modes
Safety/ethics considerations
Versioning and change history
Licensing and usage terms
Contact/support information
Deployment requirements and constraints
Q8
Multiple Choice
In the past 3 months, how often did you consult model documentation when integrating models?
Every integration
Most integrations
Sometimes
Rarely
Never
Not applicable — I haven't integrated models recently
Q9
Opinion Scale
In model cards you've used, how easy was it to locate limitations and failure modes?
Range: 1 – 7
Min: Very difficultMid: NeutralMax: Very easy
Q10
Opinion Scale
In general, how easy do you think it would be to find a model's limitations in a typical model card?
Range: 1 – 7
Min: Very difficultMid: NeutralMax: Very easy
Q11
Opinion Scale
How confident are you in using a model card to judge a model's suitability for a safety-critical deployment (e.g., healthcare, autonomous systems)?
Range: 1 – 7
Min: Not at all confidentMid: NeutralMax: Extremely confident
Q12
Opinion Scale
How confident are you in using a model card to judge a model's suitability for a fairness-sensitive application (e.g., hiring, credit scoring)?
Range: 1 – 7
Min: Not at all confidentMid: NeutralMax: Extremely confident
Q13
Opinion Scale
How confident are you in using a model card to judge a model's suitability for a latency-sensitive production system (e.g., real-time inference)?
Range: 1 – 7
Min: Not at all confidentMid: NeutralMax: Extremely confident
Q14
Multiple Choice
Have you ever discovered a model limitation that was not documented in its model card?
Yes
No
Not sure
Q15
Long Text
Please briefly describe the undocumented limitation and how you identified it.
Max chars
Q16
Ranking
Rank the following limitation factors from most to least important when selecting a model.
Drag to order (top = most important)
Accuracy on out-of-distribution data
Biased outcomes for specific subgroups
Privacy or data leakage risk
Robustness to adversarial or prompt attacks
Interpretability/traceability gaps
Q17
Multiple Choice
When limitations are unclear or missing, what do you typically do? Select all that apply.
Run targeted tests or benchmarks
Search issues/forums or community reports
Contact provider or open a ticket
Read source paper or repository docs
Switch to a different model
Proceed with extra monitoring/guardrails
Defer or block the integration
Other (please specify)
Q18
Multiple Choice
Which format would make model limitations most actionable for you?
One-page summary with key facts
Table with metrics by subgroup
Risk checklist with mitigations
Traffic-light risk labeling
Interactive examples and failure cases
Link to detailed paper/appendix
Other (please specify)
Q19
Opinion Scale
Overall, how much do you trust model cards to accurately represent a model's capabilities and limitations?
Range: 1 – 7
Min: Do not trust at allMid: NeutralMax: Trust completely
Q20
Long Text
Based on your responses in this survey, do you have any suggestions to make model cards clearer or more actionable?
Max chars
Q21
AI Interview
We'd like to explore your experiences with model cards a bit further. Our AI moderator will ask a couple of follow-up questions based on your earlier responses.
AI InterviewLength: 2Personality: [Object Object]Mode: Fast
Reference questions: 6
Q22
Dropdown
What is your primary role?
Backend/Full-stack Engineer
ML/AI Engineer
Data Scientist/Analyst
Researcher
Product Manager
SRE/DevOps
Security/Privacy Engineer
Technical Writer
Student
Other
Q23
Dropdown
How many years have you worked professionally with ML/AI (in any capacity)?
0–1
2–4
5–7
8–10
11+
Prefer not to say
Q24
Dropdown
What is your organization's approximate size (total employees)?
1–10
11–50
51–200
201–1,000
1,001–5,000
5,001+
Prefer not to say
Q25
Dropdown
Which region are you primarily based in?
Africa
Asia
Europe
Latin America & Caribbean
Middle East
North America
Oceania
Prefer not to say
Q26
Multiple Choice
Which programming languages do you primarily use when working with ML models? Select all that apply.
Python
JavaScript/TypeScript
Java
C/C++
Go
Rust
R
Swift/Kotlin
Other
Prefer not to say
Q27
Dropdown
Which industry best describes your work context?
Technology
Finance
Healthcare
Retail/E-commerce
Media/Entertainment
Education
Government/Nonprofit
Other
Prefer not to say
Q28
Chat Message
Thank you for your time! Your feedback will help improve how model cards communicate limitations and support better integration decisions.
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