Authors:
(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. 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 1 Introduction Data plays a critical role in machine learning. Every machine learning model is trained and evaluated using data, quite often in the form of static datasets. The characteristics of these datasets fundamentally influence a model’s behavior: a model is unlikely to perform well in the wild if its deployment context does not match its training or evaluation datasets, or if these datasets reflect unwanted societal biases. Mismatches like this can have especially severe consequences when machine learning models are used in high-stakes domains, such as criminal justice [1, 13, 24], hiring [19], critical infrastructure [11, 21], and finance [18]. Even in other domains, mismatches may lead to loss of revenue or public relations setbacks. Of particular concern are recent examples showing that machine learning models can reproduce or amplify unwanted societal biases reflected in training datasets [4, 5, 12]. For these and other reasons, the World Economic Forum suggests that all entities should document the provenance, creation, and use of machine learning datasets in order to avoid discriminatory outcomes [25]. Although data provenance has been studied extensively in the databases community [3, 8], it is rarely discussed in the machine learning community. Documenting the creation and use of datasets has received even less attention. Despite the importance of data to machine learning, there is currently no standardized process for documenting machine learning datasets. 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 describing its operating characteristics, test results, recommended usage, 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 have the potential to increase transparency and accountability within the machine learning community, mitigate unwanted societal biases in machine learning models, facilitate greater reproducibility of machine learning results, and help researchers and practitioners to select more appropriate datasets for their chosen tasks. After outlining our objectives below, we describe the process by which we developed datasheets for datasets. We then provide a set of questions designed to elicit the information that a datasheet for a dataset might contain, as well as a workflow for dataset creators to use when answering these questions. We conclude with a summary of the impact to date of datasheets for datasets and a discussion of implementation challenges and avenues for future work. This paper is available on arxiv under CC 4.0 license. Authors: (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. Authors: Authors: (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. 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 1 Introduction Data plays a critical role in machine learning. Every machine learning model is trained and evaluated using data, quite often in the form of static datasets. The characteristics of these datasets fundamentally influence a model’s behavior: a model is unlikely to perform well in the wild if its deployment context does not match its training or evaluation datasets, or if these datasets reflect unwanted societal biases. Mismatches like this can have especially severe consequences when machine learning models are used in high-stakes domains, such as criminal justice [1, 13, 24], hiring [19], critical infrastructure [11, 21], and finance [18]. Even in other domains, mismatches may lead to loss of revenue or public relations setbacks. Of particular concern are recent examples showing that machine learning models can reproduce or amplify unwanted societal biases reflected in training datasets [4, 5, 12]. For these and other reasons, the World Economic Forum suggests that all entities should document the provenance, creation, and use of machine learning datasets in order to avoid discriminatory outcomes [25]. Although data provenance has been studied extensively in the databases community [3, 8], it is rarely discussed in the machine learning community. Documenting the creation and use of datasets has received even less attention. Despite the importance of data to machine learning, there is currently no standardized process for documenting machine learning datasets. 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 describing its operating characteristics, test results, recommended usage, 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 have the potential to increase transparency and accountability within the machine learning community, mitigate unwanted societal biases in machine learning models, facilitate greater reproducibility of machine learning results, and help researchers and practitioners to select more appropriate datasets for their chosen tasks. After outlining our objectives below, we describe the process by which we developed datasheets for datasets. We then provide a set of questions designed to elicit the information that a datasheet for a dataset might contain, as well as a workflow for dataset creators to use when answering these questions. We conclude with a summary of the impact to date of datasheets for datasets and a discussion of implementation challenges and avenues for future work. 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

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