Silencing in data science practices
This article examines the relationship between data science practices and epistemic injustice, with a particular focus on the phenomenon of silencing . Our practice-oriented analysis of the data science pipeline – data collection, cleaning, model training and implementation – reveals a vicious cycle...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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SAGE Publishing
2025-09-01
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| Series: | Big Data & Society |
| Online Access: | https://doi.org/10.1177/20539517251365228 |
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| _version_ | 1849229040567713792 |
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| author | Ida Marie S Lassen Jens Christian Bjerring Kristoffer L Nielbo |
| author_facet | Ida Marie S Lassen Jens Christian Bjerring Kristoffer L Nielbo |
| author_sort | Ida Marie S Lassen |
| collection | DOAJ |
| description | This article examines the relationship between data science practices and epistemic injustice, with a particular focus on the phenomenon of silencing . Our practice-oriented analysis of the data science pipeline – data collection, cleaning, model training and implementation – reveals a vicious cycle of silencing that perpetuates and amplifies existing biases. We demonstrate how initial biases in data collection can lead to the development of models that silence minority voices and how, once deployed, these models further marginalise these groups. Importantly, we argue that the relationship between data science and epistemic injustice is not inherently negative – data science methods can detect biases, mitigate injustices and translate critical reflections into specifications for inclusive systems. By bridging discussions in data science and the philosophy of epistemic injustice, this article contributes to the ongoing discourse on the ethical implications of big data and artificial intelligence, underscoring the importance of embedding epistemic justice considerations throughout the data science lifecycle. |
| format | Article |
| id | doaj-art-b6dda3b9f5d648329b11fdb13c9c2032 |
| institution | Kabale University |
| issn | 2053-9517 |
| language | English |
| publishDate | 2025-09-01 |
| publisher | SAGE Publishing |
| record_format | Article |
| series | Big Data & Society |
| spelling | doaj-art-b6dda3b9f5d648329b11fdb13c9c20322025-08-22T09:03:20ZengSAGE PublishingBig Data & Society2053-95172025-09-011210.1177/20539517251365228Silencing in data science practicesIda Marie S Lassen0Jens Christian Bjerring1 Kristoffer L Nielbo2 Center for Humanities Computing, , Aarhus, Denmark Department of Philosophy, , Aarhus, Denmark Center for Humanities Computing, , Aarhus, DenmarkThis article examines the relationship between data science practices and epistemic injustice, with a particular focus on the phenomenon of silencing . Our practice-oriented analysis of the data science pipeline – data collection, cleaning, model training and implementation – reveals a vicious cycle of silencing that perpetuates and amplifies existing biases. We demonstrate how initial biases in data collection can lead to the development of models that silence minority voices and how, once deployed, these models further marginalise these groups. Importantly, we argue that the relationship between data science and epistemic injustice is not inherently negative – data science methods can detect biases, mitigate injustices and translate critical reflections into specifications for inclusive systems. By bridging discussions in data science and the philosophy of epistemic injustice, this article contributes to the ongoing discourse on the ethical implications of big data and artificial intelligence, underscoring the importance of embedding epistemic justice considerations throughout the data science lifecycle.https://doi.org/10.1177/20539517251365228 |
| spellingShingle | Ida Marie S Lassen Jens Christian Bjerring Kristoffer L Nielbo Silencing in data science practices Big Data & Society |
| title | Silencing in data science practices |
| title_full | Silencing in data science practices |
| title_fullStr | Silencing in data science practices |
| title_full_unstemmed | Silencing in data science practices |
| title_short | Silencing in data science practices |
| title_sort | silencing in data science practices |
| url | https://doi.org/10.1177/20539517251365228 |
| work_keys_str_mv | AT idamarieslassen silencingindatasciencepractices AT jenschristianbjerring silencingindatasciencepractices AT kristofferlnielbo silencingindatasciencepractices |