Datasheets for Datasets

TL;DR

This paper introduces a standardized documentation format called datasheets for datasets, aiming to improve transparency, accountability, and communication in machine learning dataset creation and usage.

Contribution

It proposes a structured datasheet template for datasets, inspired by electronics datasheets, to document motivation, composition, collection, and recommended uses.

Findings

Enhances transparency and accountability in dataset documentation.

Facilitates better communication between dataset creators and users.

Encourages adoption of standardized dataset documentation practices.

Abstract

The machine learning community currently has no standardized process for documenting datasets, which can lead to severe consequences in high-stakes domains. To address this gap, we propose datasheets for datasets. In the electronics industry, every component, no matter how simple or complex, is accompanied with a datasheet that describes its operating characteristics, test results, recommended uses, and other information. By analogy, we propose that every dataset be accompanied with a datasheet that documents its motivation, composition, collection process, recommended uses, and so on. Datasheets for datasets will facilitate better communication between dataset creators and dataset consumers, and encourage the machine learning community to prioritize transparency and accountability.