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Developer Survey Template: AI Model Cards & Limitations

Assess developer knowledge of AI model cards, test limitation communication, and learn how it impacts trust, risk, and adoption. Ready-to-use survey template.

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Intelligent follow-up questions based on responses

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Detailed Reports

Comprehensive insights and recommendations

Sample Survey Items

Q1
Chat Message
Welcome! This short survey asks about your experience with model cards and how model limitations influence your workflow. Please answer based on your own experience.
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
Dropdown
Attention check: To confirm you're paying attention, please select "Blue" from the list.
  • Red
  • Green
  • Blue
  • Yellow
  • Purple
Q4
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
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.
Q6
Multiple Choice
Based on that description, 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
Q7
Rating
If a model card is available, how likely are you to read it before using the model?
Scale: 11 (star)
Min: Very unlikelyMax: Very likely
Q8
Multiple Choice
From the model cards you’ve seen, 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
Q9
Opinion Scale
How easy do you think it would be to find a model’s limitations in a typical model card?
Range: 1 – 10
Min: Very hardMid: NeutralMax: Very easy
Q10
Opinion Scale
In model cards you’ve used, how easy was it to locate limitations and failure modes?
Range: 1 – 10
Min: Very hardMid: NeutralMax: Very easy
Q11
Matrix
How confident are you in judging model suitability (using a model card) for these scenarios?
RowsVery unconfidentUnconfidentNeutralConfidentVery confident
Moderating user-generated images•••••
Multilingual sentiment analysis•••••
Conversational QA chatbot for customer support•••••
Named-entity recognition on noisy text•••••
Medical triage suggestions (non-diagnostic)•••••
Q12
Multiple Choice
Have you discovered a model limitation that was not documented in its model card?
  • Yes
  • No
  • Not sure
Q13
Long Text
If yes, briefly describe what was missing and how you identified the limitation.
Max 600 chars
Q14
Ranking
Rank the following limitation factors from most to least important when selecting a model.
Drag to order (top = most important)
  1. Accuracy on out-of-distribution data
  2. Biased outcomes for specific subgroups
  3. Privacy or data leakage risk
  4. Robustness to adversarial or prompt attacks
  5. Latency/throughput constraints
  6. Interpretability/traceability gaps
  7. Licensing or usage restrictions
Q15
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
Q16
Multiple Choice
In the past 3 months, how often did you consult model documentation when integrating models?
  • Every integration
  • Most integrations
  • Sometimes
  • Rarely
  • Never
Q17
Dropdown
Which format would make 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
Q18
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
Q19
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
Q20
Dropdown
What is your organization size?
  • 1–10
  • 11–50
  • 51–200
  • 201–1,000
  • 1,001–5,000
  • 5,001+
  • Prefer not to say
Q21
Dropdown
Which region are you primarily based in?
  • Africa
  • Asia
  • Europe
  • Latin America & Caribbean
  • Middle East
  • North America
  • Oceania
  • Prefer not to say
Q22
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
Q23
Dropdown
Which industry best describes your work context?
  • Technology
  • Finance
  • Healthcare
  • Retail/E-commerce
  • Media/Entertainment
  • Education
  • Government/Nonprofit
  • Other
  • Prefer not to say
Q24
Long Text
Any suggestions to make model cards clearer or more actionable?
Max 600 chars
Q25
AI Interview
AI Interview: 2 Follow-up Questions on model cards and limitations
AI InterviewLength: 2Personality: [Object Object]Mode: Fast
Q26
Chat Message
Thanks for your time—your feedback will help improve how model cards communicate limitations and fit for use.

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