Explore the composition of datasets, including instances, labels, data splits, errors, external resources, and considerations for confidentiality & sensitivity
Authors: TIMNIT GEBRU, Black in AI; JAMIE MORGENSTERN, University of Washington; BRIANA VECCHIONE, Cornell University; JENNIFER WORTMAN VAUGHAN, Microsoft Research; HANNA WALLACH, Microsoft Research; HAL DAUMÉ III, Microsoft Research; University of Maryland; KATE CRAWFORD, Microsoft Research.
Table of Links 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 Appendix 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 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 ? Are there multiple types of instances ? Please provide a description. • How many instances are there in total ? • Does the dataset contain all possible instances or is it a sample 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 ? If so, please describe how this representativeness was validated/verified. If it is not representative of the larger set, please describe why not . • What data does each instance consist of? “Raw” data 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 . This does not include intentionally removed information, but might include, e.g., redacted text. • Are relationships between individual instances made explicit ? If so, please describe how these relationships are made explicit. • Are there recommended data splits ? 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 ? 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 ; c) are there any restrictions 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 ? 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 ? 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 , either directly or indirectly from the dataset? If so, please describe how. • Does the dataset contain data that might be considered sensitive in any way ? If so, please provide a description. • Any other comments? This paper is available on arxiv under CC 4.0 license. Authors: TIMNIT GEBRU, Black in AI; JAMIE MORGENSTERN, University of Washington; BRIANA VECCHIONE, Cornell University; JENNIFER WORTMAN VAUGHAN, Microsoft Research; HANNA WALLACH, Microsoft Research; HAL DAUMÉ III, Microsoft Research; University of Maryland; KATE CRAWFORD, Microsoft Research. Authors: Authors: TIMNIT GEBRU, Black in AI; JAMIE MORGENSTERN, University of Washington; BRIANA VECCHIONE, Cornell University; JENNIFER WORTMAN VAUGHAN, Microsoft Research; HANNA WALLACH, Microsoft Research; HAL DAUMÉ III, Microsoft Research; University of Maryland; KATE CRAWFORD, Microsoft Research. Table of Links 1 Introduction 1 Introduction 1.1 Objectives 1.1 Objectives 2 Development Process 2 Development Process 3 Questions and Workflow 3 Questions and Workflow 3.1 Motivation 3.1 Motivation 3.2 Composition 3.2 Composition 3.3 Collection Process 3.3 Collection Process 3.4 Preprocessing/cleaning/labeling 3.4 Preprocessing/cleaning/labeling 3.5 Uses 3.5 Uses 3.6 Distribution 3.6 Distribution 3.7 Maintenance 3.7 Maintenance 4 Impact and Challenges 4 Impact and Challenges Acknowledgments and References Acknowledgments and References Appendix Appendix 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 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 ? Are there multiple types of instances ? Please provide a description. • What do the instances that comprise the dataset represent ? • How many instances are there in total ? • How many instances are there in total ? • Does the dataset contain all possible instances or is it a sample 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 ? If so, please describe how this representativeness was validated/verified. If it is not representative of the larger set, please describe why not . • Does the dataset contain all possible instances or is it a sample of instances from a larger set? • What data does each instance consist of? “Raw” data or features? In either case, please provide a description. • What data does each instance consist of? • Is there a label or target associated with each instance? If so, please provide a description. • Is there a label or target associated with each instance? • Is any information missing from individual instances? If so, please provide a description, explaining why this information is missing . This does not include intentionally removed information, but might include, e.g., redacted text. • Is any information missing from individual instances? • Are relationships between individual instances made explicit ? If so, please describe how these relationships are made explicit. • Are relationships between individual instances made explicit ? • Are there recommended data splits ? If so, please provide a description of these splits, explaining the rationale behind them. • Are there recommended data splits ? • Are there any errors, sources of noise, or redundancies in the dataset? If so, please provide a description. • 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 ? 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 ; c) are there any restrictions 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. • Is the dataset self-contained, or does it link to or otherwise rely on external resources ? • Does the dataset contain data that might be considered confidential ? If so, please provide a description. • Does the dataset contain data that might be considered confidential ? • Does the dataset contain data that, if viewed directly, might be offensive, insulting, threatening, or might otherwise cause anxiety? If so, please describe why. • Does the dataset contain data that, if viewed directly, might be offensive, insulting, threatening, or might otherwise cause anxiety? If the dataset does not relate to people, you may skip the remaining questions in this section. • Does the dataset identify any subpopulations ? If so, please describe how these subpopulations are identified and provide a description of their respective distributions within the dataset. • Does the dataset identify any subpopulations ? • Is it possible to identify individuals , either directly or indirectly from the dataset? If so, please describe how. • Is it possible to identify individuals , either directly or indirectly from the dataset? • Does the dataset contain data that might be considered sensitive in any way ? If so, please provide a description. • Does the dataset contain data that might be considered sensitive in any way ? • Any other comments? • Any other comments? This paper is available on arxiv under CC 4.0 license. This paper is available on arxiv under CC 4.0 license. available on arxiv
Please follow us on Google to support usUnited States Latest News, United States Headlines
Similar News: You can also read news stories similar to this one that we have collected from other news sources.
VEATIC: Video-based Emotion and Affect Tracking in Context Dataset: VEATIC DatasetIn this paper, researchers introduce VEATIC dataset for human affect recognition, addressing limitations in existing datasets, enabling context-based inference.
Read more »
ChipNeMo: Domain-Adapted LLMs for Chip Design: DatasetResearchers present ChipNeMo, using domain adaptation to enhance LLMs for chip design, achieving up to 5x model size reduction with better performance.
Read more »
Solos: A Dataset for Audio-Visual Music AnalysisIn this paper, researchers introduce Solos, a clean dataset of solo musical performances for training machine learning models on various audio-visual tasks.
Read more »
Solos: A Dataset for Audio-Visual Music Analysis- Conclusions and ReferencesIn this paper, researchers introduce Solos, a clean dataset of solo musical performances for training machine learning models on various audio-visual tasks.
Read more »
Solos: A Dataset for Audio-Visual Music AnalysisIn this paper, researchers introduce Solos, a clean dataset of solo musical performances for training machine learning models on various audio-visual tasks.
Read more »
Solos: A Dataset for Audio-Visual Music Analysis - Abstract and IntroIn this paper, researchers introduce Solos, a clean dataset of solo musical performances for training machine learning models on various audio-visual tasks.
Read more »