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What’s the best way to document model limitations in a model card?

Asked on Oct 13, 2025

Answer

Documenting model limitations in a model card is crucial for transparency and responsible AI deployment. The model card should clearly outline any known limitations, including performance issues, biases, and contexts where the model may not perform well. This helps users understand the model's constraints and make informed decisions about its use.

<!-- BEGIN COPY / PASTE -->
    **Model Limitations:****Performance Variability:** The model may underperform with non-English text inputs.
**Bias Concerns:** Tends to favor majority class in imbalanced datasets.
**Contextual Constraints:** Not suitable for real-time decision-making due to latency.
**Data Limitations:** Trained on data from 2018-2020, may not generalize to post-2020 trends.
    <!-- END COPY / PASTE -->

Additional Comment:

Ensure limitations are specific and actionable, providing enough detail for users to assess risks.
Regularly update the model card as new limitations are discovered or addressed.
Include any mitigation strategies or workarounds if applicable.
Consider using a standardized framework like the NIST AI Risk Management Framework for consistency.

✅ Answered with AI Ethics best practices.

Didn’t find the answer you were looking for?

What’s the best way to document model limitations in a model card?

Asked on Oct 13, 2025

Answer

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