Model Cards for Model Reporting

Abstract

Trained machine learning models are increasingly used to perform high-impact tasks in areas such as law enforcement, medicine, education, and employment. In order to clarify the intended use cases of machine learning models and minimize their usage in contexts for which they are not well suited, we recommend that released models be accompanied by documentation detailing their performance characteristics. In this paper, we propose a framework that we call model cards, to encourage such transparent model reporting. Model cards are short documents accompanying trained machine learning 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. While we focus primarily on human-centered machine learning models in the application fields of computer vision and natural language processing, this framework can be used to document any trained machine learning model. To solidify the concept, we provide cards for two supervised models: One trained to detect smiling faces in images, and one trained to detect toxic comments in text. We propose model cards as a step towards the responsible democratization of machine learning and related AI technology, increasing transparency into how well AI technology works. We hope this work encourages those releasing trained machine learning models to accompany model releases with similar detailed evaluation numbers and other relevant documentation.

The authors' own abstract, copied from arxiv.org.

How to cite it

Built from the details listed on this page. Check them against the paper before you submit.

Mitchell, M., Wu, S., Zaldivar, A., Barnes, P., Vasserman, L., et al. (2018). Model Cards for Model Reporting. Preprint, not yet peer-reviewed. https://arxiv.org/abs/1810.03993

Mitchell, Margaret, et al. "Model Cards for Model Reporting." Preprint, not yet peer-reviewed, 2018, https://arxiv.org/abs/1810.03993.

@article{mitchell2018model,
  title = {Model Cards for Model Reporting},
  author = {Margaret Mitchell and Simone Wu and Andrew Zaldivar and Parker Barnes and Lucy Vasserman and others},
  journal = {Preprint, not yet peer-reviewed},
  year = {2018},
  url = {https://arxiv.org/abs/1810.03993},
}

What it is

Proposes a short standard document to ship with every trained model: what it is for, who it was tested on, where it performs worse, and what it should not be used for. Most model documentation you see today descends from this. Free on arXiv.

Topics