The State of Documentation Practices of Third-party Machine Learning
  Models and Datasets
Ernesto Lang Oreamuno Rohan Faiyaz Khan Abdul Ali Bangash Catherine Stinson Bram Adams
Model stores offer third-party ML models and datasets for easy project
integration, minimizing coding efforts. One might hope to find detailed
specifications of these models and datasets in the documentation, leveraging
documentation standards such as model and dataset cards. In this study, we use
statistical analysis and hybrid card sorting to assess the state of the
practice of documenting model cards and dataset cards in one of the largest
model stores in use today--Hugging Face (HF). Our findings show that only
21,902 models (39.62\%) and 1,925 datasets (28.48\%) have documentation.
Furthermore, we observe inconsistency in ethics and transparency-related
documentation for ML models and datasets.