Skip to content
New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Jump to bottom

fix typo README.md #21

Open
wants to merge 1 commit into
base: main
Choose a base branch
from
Open

fix typo README.md #21

Changes from all commits
Commits
Show all changes
1 commit
Select commit
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
2 changes: 1 addition & 1 deletion templates/README.md
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -3,7 +3,7 @@
| --- | --- | --- | --- | --- |
| 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](https://dl.acm.org/doi/10.1145/3531146.3533231) | 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](https://arxiv.org/abs/1810.03993) | 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](https://arxiv.org/abs/2202.13028) | New in 2022 |
| Healthsheet| To begin addressing gaps in current practices, frameworks, and standards for the ethical collection of health data for 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](https://arxiv.org/abs/2202.13028) | New in 2022 |


### FAQs
Expand Down