| Type | Description | Use to Document | Link | Comments |
|---|---|---|---|---|
| Data Card | Data Cards are structured summaries of essential facts about various aspects of ML datasets needed by stakeholders across a dataset's lifecycle for responsible AI development. These summaries provide explanations of processes and rationales that shape the data and consequently the models—such as upstream sources, data collection and annotation methods; training and evaluation methods, intended use; or decisions affecting model performance. | General Purpose Datasets at scale | Research Paper | New in 2022 |
| Model Card | Model cards are short documents accompanying trained machinelearning models that provide benchmarked evaluation in a variety of conditions, such as across different cultural, demographic, or phenotypic groups (e.g., race, geographic location, sex, Fitzpatrick skin type) and intersectional groups (e.g., age and race, or sex and Fitzpatrick skin type) that are relevant to the intended application domains. Model cards also disclose the context in which models are intended to be used, details of the performance evaluation procedures, and other relevant information. | General Purpose Models | Research Paper | Updated in 2021 |
| Healthsheet | To begin addressing gaps in current practices, frameworks, and standards for the ethical collection of health datafor ML, we introduce a contextualized type of datasheet that attends to the needs of healthcare data: Healthsheet. The purpose of Healthsheet is to contribute to the meaningful ethical review of healthcare data, in addition to existing data governance practices or legal requirements of healthcare data. Further, it aligns with recent initiatives in clinical trials data collection and data-driven digital health technologies. | Healthcare datasets, domain-specialized datasets | Research Paper | New in 2022 |
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