Mind the data gap(s): Investigating power in speech and language datasets

Nina Markl

doi:10.18653/v1/2022.ltedi-1.1

Mind the data gap(s): Investigating power in speech and language datasets

Nina Markl

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract / Description of output

Algorithmic oppression is an urgent and persistent problem in speech and language technologies. Considering power relations embedded in datasets before compiling or using them to train or test speech and language technologies is essential to designing less harmful, more just technologies. This paper presents a reflective exercise to recognise and challenge gaps and the power relations they reveal in speech and language datasets by applying principles of Data Feminism and Design Justice, and building on work on dataset documentation and sociolinguistics.

Original language	English
Title of host publication	Proceedings of the Second Workshop on Language Technology for Equality, Diversity and Inclusion
Editors	Bharathi Raja Chakravarthi, B Bharathi, John P. McCrae, Manel Zarrouk, Kalika Bali, Paul Buitelaar
Place of Publication	Dublin, Ireland
Publisher	Association for Computational Linguistics
Pages	1-12
Number of pages	12
ISBN (Electronic)	978-1-955917-43-8
DOIs	https://doi.org/10.18653/v1/2022.ltedi-1.1
Publication status	Published - 3 Jun 2022
Event	2nd Workshop on Language Technology for Equality, Diversity, Inclusion 2022 - Dublin, Ireland Duration: 27 May 2022 → 27 May 2022 Conference number: 2 https://sites.google.com/view/lt-edi-2022/home

Workshop

Workshop	2nd Workshop on Language Technology for Equality, Diversity, Inclusion 2022
Abbreviated title	LT-EDI 2022
Country/Territory	Ireland
City	Dublin
Period	27/05/22 → 27/05/22
Internet address	https://sites.google.com/view/lt-edi-2022/home

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Mind the data_MARKL_DOA26032022_VOR_CC-BYFinal published version, 184 KBLicence: Creative Commons: Attribution (CC-BY)

https://aclanthology.org/2022.ltedi-1.1Licence: Creative Commons: Attribution (CC-BY)

Cite this

Markl, N. (2022). Mind the data gap(s): Investigating power in speech and language datasets. In B. R. Chakravarthi, B. Bharathi, J. P. McCrae, M. Zarrouk, K. Bali, & P. Buitelaar (Eds.), Proceedings of the Second Workshop on Language Technology for Equality, Diversity and Inclusion (pp. 1-12). Association for Computational Linguistics. https://doi.org/10.18653/v1/2022.ltedi-1.1

Markl, Nina. / Mind the data gap(s): Investigating power in speech and language datasets. Proceedings of the Second Workshop on Language Technology for Equality, Diversity and Inclusion. editor / Bharathi Raja Chakravarthi ; B Bharathi ; John P. McCrae ; Manel Zarrouk ; Kalika Bali ; Paul Buitelaar. Dublin, Ireland : Association for Computational Linguistics, 2022. pp. 1-12

@inproceedings{21a520a4b29d4fc782421cc2a0d23676,

title = "Mind the data gap(s): Investigating power in speech and language datasets",

abstract = "Algorithmic oppression is an urgent and persistent problem in speech and language technologies. Considering power relations embedded in datasets before compiling or using them to train or test speech and language technologies is essential to designing less harmful, more just technologies. This paper presents a reflective exercise to recognise and challenge gaps and the power relations they reveal in speech and language datasets by applying principles of Data Feminism and Design Justice, and building on work on dataset documentation and sociolinguistics.",

author = "Nina Markl",

note = "Funding Information: This work was supported by the UKRI Centre for Doctoral Training in Natural Language Processing, funded by the UKRI (grant EP/S022481/1) and the University of Edinburgh, School of Informatics and School of Philosophy, Psychology & Language Sciences. I{\textquoteright}d like to thank Catherine Lai, Lauren Hall-Lew, Gilly Marchini, Stephen McNulty and three anonymous reviewers for their comments. Funding Information: Q: Why are some varieties and speakers excluded or underrepresented? A: The skew towards young, highly educated, first language speakers of English is probably the result of the sampling method: speakers were primarily recruited via universities and personal networks of researchers. Q: Why are some genres/topics/styles excluded or underrepresented? A: Even though the speech style is more conversational and naturalistic than in other corpora (e.g. read speech in TIMIT), it might still be quite formal because the interlocutors don{\textquoteright}t know each other. Q: How are speakers and annotators trained, paid, rewarded and acknowledged? A: Speakers were paid after participation (the documentation does not mention the sum). Recordings were checked for audio quality, transcribed and annotated by members of the research team. Q: Who funds the dataset compilation? A: The compilation of Switchboard was funded by the US Department of Defense. Publisher Copyright: {\textcopyright} 2022 Association for Computational Linguistics.; 2nd Workshop on Language Technology for Equality, Diversity, Inclusion 2022, LT-EDI 2022 ; Conference date: 27-05-2022 Through 27-05-2022",

year = "2022",

month = jun,

day = "3",

doi = "10.18653/v1/2022.ltedi-1.1",

language = "English",

pages = "1--12",

editor = "Chakravarthi, {Bharathi Raja} and B Bharathi and McCrae, {John P.} and Manel Zarrouk and Kalika Bali and Paul Buitelaar",

booktitle = "Proceedings of the Second Workshop on Language Technology for Equality, Diversity and Inclusion",

publisher = "Association for Computational Linguistics",

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Markl, N 2022, Mind the data gap(s): Investigating power in speech and language datasets. in BR Chakravarthi, B Bharathi, JP McCrae, M Zarrouk, K Bali & P Buitelaar (eds), Proceedings of the Second Workshop on Language Technology for Equality, Diversity and Inclusion. Association for Computational Linguistics, Dublin, Ireland, pp. 1-12, 2nd Workshop on Language Technology for Equality, Diversity, Inclusion 2022, Dublin, Ireland, 27/05/22. https://doi.org/10.18653/v1/2022.ltedi-1.1

Mind the data gap(s): Investigating power in speech and language datasets. / Markl, Nina.
Proceedings of the Second Workshop on Language Technology for Equality, Diversity and Inclusion. ed. / Bharathi Raja Chakravarthi; B Bharathi; John P. McCrae; Manel Zarrouk; Kalika Bali; Paul Buitelaar. Dublin, Ireland: Association for Computational Linguistics, 2022. p. 1-12.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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AB - Algorithmic oppression is an urgent and persistent problem in speech and language technologies. Considering power relations embedded in datasets before compiling or using them to train or test speech and language technologies is essential to designing less harmful, more just technologies. This paper presents a reflective exercise to recognise and challenge gaps and the power relations they reveal in speech and language datasets by applying principles of Data Feminism and Design Justice, and building on work on dataset documentation and sociolinguistics.

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BT - Proceedings of the Second Workshop on Language Technology for Equality, Diversity and Inclusion

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T2 - 2nd Workshop on Language Technology for Equality, Diversity, Inclusion 2022

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Markl N. Mind the data gap(s): Investigating power in speech and language datasets. In Chakravarthi BR, Bharathi B, McCrae JP, Zarrouk M, Bali K, Buitelaar P, editors, Proceedings of the Second Workshop on Language Technology for Equality, Diversity and Inclusion. Dublin, Ireland: Association for Computational Linguistics. 2022. p. 1-12 doi: 10.18653/v1/2022.ltedi-1.1

Mind the data gap(s): Investigating power in speech and language datasets

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Workshop

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