research-article

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Augmented Datasheets for Speech Datasets and Ethical Decision-Making

Authors:

Orestis Papakyriakopoulos,

Anna Seo Gyeong Choi,

Jerone Andrews,

Rebecca Bourke,

Allison KoeneckeAuthors Info & Claims

FAccT '23: Proceedings of the 2023 ACM Conference on Fairness, Accountability, and Transparency

Pages 881 - 904

https://doi.org/10.1145/3593013.3594049

Published: 12 June 2023 Publication History

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Abstract

Speech datasets are crucial for training Speech Language Technologies (SLT); however, the lack of diversity of the underlying training data can lead to serious limitations in building equitable and robust SLT products, especially along dimensions of language, accent, dialect, variety, and speech impairment—and the intersectionality of speech features with socioeconomic and demographic features. Furthermore, there is often a lack of oversight on the underlying training data—commonly built on massive web-crawling and/or publicly available speech—with regard to the ethics of such data collection. To encourage standardized documentation of such speech data components, we introduce an augmented datasheet for speech datasets1, which can be used in addition to “Datasheets for Datasets” [78]. We then exemplify the importance of each question in our augmented datasheet based on in-depth literature reviews of speech data used in domains such as machine learning, linguistics, and health. Finally, we encourage practitioners—ranging from dataset creators to researchers—to use our augmented datasheet to better define the scope, properties, and limits of speech datasets, while also encouraging consideration of data-subject protection and user community empowerment. Ethical dataset creation is not a one-size-fits-all process, but dataset creators can use our augmented datasheet to reflexively consider the social context of related SLT applications and data sources in order to foster more inclusive SLT products downstream.

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Index Terms

Augmented Datasheets for Speech Datasets and Ethical Decision-Making
1. Applied computing
  1. Arts and humanities
    1. Sound and music computing
2. Human-centered computing
  1. Collaborative and social computing
    1. Collaborative and social computing design and evaluation methods

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