Understanding Dataset Instances and Relationships

10 Jun 2024


(1) TIMNIT GEBRU, Black in AI;

(2) JAMIE MORGENSTERN, University of Washington;

(3) BRIANA VECCHIONE, Cornell University;

(4) JENNIFER WORTMAN VAUGHAN, Microsoft Research;

(5) HANNA WALLACH, Microsoft Research;

(6) HAL DAUMÉ III, Microsoft Research; University of Maryland;

(7) KATE CRAWFORD, Microsoft Research.

1 Introduction

1.1 Objectives

2 Development Process

3 Questions and Workflow

3.1 Motivation

3.2 Composition

3.3 Collection Process

3.4 Preprocessing/cleaning/labeling

3.5 Uses

3.6 Distribution

3.7 Maintenance

4 Impact and Challenges

Acknowledgments and References


3.2 Composition

Dataset creators should read through these questions prior to any data collection and then provide answers once data collection is complete. Most of the questions in this section are intended to provide dataset consumers with the information they need to make informed decisions about using the dataset for their chosen tasks. Some of the questions are designed to elicit information about compliance with the EU’s General Data Protection Regulation (GDPR) or comparable regulations in other jurisdictions.

Questions that apply only to datasets that relate to people are grouped together at the end of the section. We recommend taking a broad interpretation of whether a dataset relates to people. For example, any dataset containing text that was written by people relates to people.

• What do the instances that comprise the dataset represent (e.g., documents, photos, people, countries)? Are there multiple types of instances (e.g., movies, users, and ratings; people and interactions between them; nodes and edges)? Please provide a description.

• How many instances are there in total (of each type, if appropriate)?

• Does the dataset contain all possible instances or is it a sample (not necessarily random) of instances from a larger set? If the dataset is a sample, then what is the larger set? Is the sample representative of the larger set (e.g., geographic coverage)? If so, please describe how this representativeness was validated/verified. If it is not representative of the larger set, please describe why not (e.g., to cover a more diverse range of instances, because instances were withheld or unavailable).

• What data does each instance consist of? “Raw” data (e.g., unprocessed text or images) or features? In either case, please provide a description.

• Is there a label or target associated with each instance? If so, please provide a description.

• Is any information missing from individual instances? If so, please provide a description, explaining why this information is missing (e.g., because it was unavailable). This does not include intentionally removed information, but might include, e.g., redacted text.

• Are relationships between individual instances made explicit (e.g., users’ movie ratings, social network links)? If so, please describe how these relationships are made explicit.

• Are there recommended data splits (e.g., training, development/validation, testing)? If so, please provide a description of these splits, explaining the rationale behind them.

• Are there any errors, sources of noise, or redundancies in the dataset? If so, please provide a description.

• Is the dataset self-contained, or does it link to or otherwise rely on external resources (e.g., websites, tweets, other datasets)? If it links to or relies on external resources, a) are there guarantees that they will exist, and remain constant, over time; b) are there official archival versions of the complete dataset (i.e., including the external resources as they existed at the time the dataset was created); c) are there any restrictions (e.g., licenses, fees) associated with any of the external resources that might apply to a dataset consumer? Please provide descriptions of all external resources and any restrictions associated with them, as well as links or other access points, as appropriate.

• Does the dataset contain data that might be considered confidential (e.g., data that is protected by legal privilege or by doctor– patient confidentiality, data that includes the content of individuals’ non-public communications)? If so, please provide a description.

• Does the dataset contain data that, if viewed directly, might be offensive, insulting, threatening, or might otherwise cause anxiety? If so, please describe why.

If the dataset does not relate to people, you may skip the remaining questions in this section.

• Does the dataset identify any subpopulations (e.g., by age, gender)? If so, please describe how these subpopulations are identified and provide a description of their respective distributions within the dataset.

• Is it possible to identify individuals (i.e., one or more natural persons), either directly or indirectly (i.e., in combination with other data) from the dataset? If so, please describe how.

• Does the dataset contain data that might be considered sensitive in any way (e.g., data that reveals race or ethnic origins, sexual orientations, religious beliefs, political opinions or union memberships, or locations; financial or health data; biometric or genetic data; forms of government identification, such as social security numbers; criminal history)? If so, please provide a description.

• Any other comments?

This paper is available on arxiv under CC 4.0 license.