Here's an initial pass, pulling from the above paper.

Model Title

Model Details.

Basic information about the model.

• Person or organization developing model
• Model date
• Model version
• Model type
• Information about training algorithms, parameters, fairness constraints or other applied approaches, and features
• Paper or other resource for more information
• Citation details
• License
• Where to send questions or comments about the model

Intended Use

Use cases that were envisioned during development.

• Primary intended uses
• Primary intended users
• Out-of-scope use cases

Factors

Factors could include demographic or phenotypic groups, environmental conditions, technical attributes, or others.

• Relevant factors
• Evaluation factors

Metrics

Metrics should be chosen to reflect potential real-world impacts of the model.

• Model performance measures
• Decision thresholds
• Variation approaches

Evaluation Data

Details on the dataset(s) used for the quantitative analyses in the card.

• Datasets
• Motivation
• Preprocessing

Training Data.

May not be possible to provide in practice. When possible, this section should mirror Evaluation Data. If such detail is not possible, minimal allowable information should be provided here, such as details of the distribution over various factors in the training datasets.

Quantitative Analyses

• Unitary results
• Intersectional results

Ethical Considerations

Caveats and Recommendations

from tasks.

Included in 2020-03-24 issue triage and cleanup.

This is published on our resources site: https://librecorps.github.io/resources/ai/model-card/

Closing as complete. 🎬

from tasks.