Model Cards for Model Reporting

by   Margaret Mitchell, et al.

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.


page 1

page 2

page 3

page 4


Using Deep Learning and Machine Learning to Detect Epileptic Seizure with Electroencephalography (EEG) Data

The prediction of epileptic seizure has always been extremely challengin...

Position Paper: Towards Transparent Machine Learning

Transparent machine learning is introduced as an alternative form of mac...

AI Usage Cards: Responsibly Reporting AI-generated Content

Given AI systems like ChatGPT can generate content that is indistinguish...

(De)Constructing Bias on Skin Lesion Datasets

Melanoma is the deadliest form of skin cancer. Automated skin lesion ana...

Is Your Toxicity My Toxicity? Exploring the Impact of Rater Identity on Toxicity Annotation

Machine learning models are commonly used to detect toxicity in online c...

Tuna-AI: tuna biomass estimation with Machine Learning models trained on oceanography and echosounder FAD data

Echo-sounder data registered by buoys attached to drifting FADs provide ...

Network Report: A Structured Description for Network Datasets

The rapid development of network science and technologies depends on sha...

Please sign up or login with your details

Forgot password? Click here to reset