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.
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
Attention check: To confirm you're paying attention, please select "Blue" from the list.
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
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.
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
If a model card is available, how likely are you to read it before using the model?
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
How easy do you think it would be to find a model’s limitations in a typical model card?
In model cards you’ve used, how easy was it to locate limitations and failure modes?
How confident are you in judging model suitability (using a model card) for these scenarios?
Have you discovered a model limitation that was not documented in its model card?
If yes, briefly describe what was missing and how you identified the limitation.
Max 600 chars
Rank the following limitation factors from most to least important when selecting a model.
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
In the past 3 months, how often did you consult model documentation when integrating models?
- Every integration
- Most integrations
- Sometimes
- Rarely
- Never
Which format would make limitations most actionable for you?
What is your primary role?
How many years have you worked professionally with ML/AI (in any capacity)?
What is your organization size?
Which region are you primarily based in?
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
Which industry best describes your work context?
Any suggestions to make model cards clearer or more actionable?
Max 600 chars
AI Interview: 2 Follow-up Questions on model cards and limitations
Thanks for your time—your feedback will help improve how model cards communicate limitations and fit for use.