Chapter 3 From Big to Democratic Data
Datasets have come to play a significant role in the technical and political realities of our overdeveloped world. This chapter indicates how invisible data processes pose a threat to the health and safety of the global public and argues for the democratic potential of data practices. This potential...
I tiakina i:
| Ngā kaituhi matua: | , |
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| Hōputu: | Online |
| Reo: | Ingarihi |
| I whakaputaina: |
Taylor & Francis
2022
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| Ngā marau: | |
| Urunga tuihono: | https://library.oapen.org/handle/20.500.12657/57277 |
| Ngā Tūtohu: |
Kāore He Tūtohu, Me noho koe te mea tuatahi ki te tūtohu i tēnei pūkete!
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| _version_ | 1869514375024345088 |
|---|---|
| author | Bunz, Mercedes Vrikki, Photini |
| author_browse | Bunz, Mercedes Vrikki, Photini |
| author_facet | Bunz, Mercedes Vrikki, Photini |
| author_sort | Bunz, Mercedes |
| collection | Directory of Open Access Books |
| description | Datasets have come to play a significant role in the technical and political realities of our overdeveloped world. This chapter indicates how invisible data processes pose a threat to the health and safety of the global public and argues for the democratic potential of data practices. This potential is set to become even more influential due to the central role data plays for training contemporary AI and technologies such as machine learning. Our case study explores the role patient datasets have for machine learning research in healthcare and shows that publicly available datasets are central to advancing data analysis research; they can act as a counterbalance to datasets full of absences, biases, and disconnects that often corrupt the quality of data. Given this, we argue for the introduction of ‘data solidarity’ as a principle of data governance and an effective critical data practice that focuses on the democratic (instead of economic) potential of data; a potential that is far too often overlooked. |
| format | Online |
| id | doab-20.500.12854ir-87655 |
| institution | Directory of Open Access Books |
| language | eng |
| publishDate | 2022 |
| publishDateRange | 2022 |
| publishDateSort | 2022 |
| publisher | Taylor & Francis |
| publisherStr | Taylor & Francis |
| record_format | ojs |
| spelling | doab-20.500.12854ir-876552025-03-12T15:59:46Z Chapter 3 From Big to Democratic Data Bunz, Mercedes Vrikki, Photini critical data practice, data as a public good, data solidarity, democratic data, data governance Datasets have come to play a significant role in the technical and political realities of our overdeveloped world. This chapter indicates how invisible data processes pose a threat to the health and safety of the global public and argues for the democratic potential of data practices. This potential is set to become even more influential due to the central role data plays for training contemporary AI and technologies such as machine learning. Our case study explores the role patient datasets have for machine learning research in healthcare and shows that publicly available datasets are central to advancing data analysis research; they can act as a counterbalance to datasets full of absences, biases, and disconnects that often corrupt the quality of data. Given this, we argue for the introduction of ‘data solidarity’ as a principle of data governance and an effective critical data practice that focuses on the democratic (instead of economic) potential of data; a potential that is far too often overlooked. 2022-07-09T04:08:18Z 2022-07-09T04:08:18Z 2022-07-08T13:37:25Z 2022 chapter https://library.oapen.org/handle/20.500.12657/57277 https://directory.doabooks.org/handle/20.500.12854/87655 eng open access image/jpeg image/jpeg image/jpeg image/jpeg Attribution 4.0 International Attribution 4.0 International Attribution 4.0 International Attribution 4.0 International https://library.oapen.org/bitstream/20.500.12657/57277/1/9781003173427_10.4324_9781003173427-3.pdf https://library.oapen.org/bitstream/20.500.12657/57277/1/9781003173427_10.4324_9781003173427-3.pdf https://library.oapen.org/bitstream/20.500.12657/57277/1/9781003173427_10.4324_9781003173427-3.pdf https://library.oapen.org/bitstream/20.500.12657/57277/1/9781003173427_10.4324_9781003173427-3.pdf Taylor & Francis Routledge 10.4324/9781003173427-3 10.4324/9781003173427-3 fa69b019-f4ee-4979-8d42-c6b6c476b5f0 Democratic Frontiers Wellcome Trust d859fbd3-d884-4090-a0ec-baf821c9abfd Wellcome Routledge 17 213552/Z/18/Z open access |
| spellingShingle | critical data practice, data as a public good, data solidarity, democratic data, data governance Bunz, Mercedes Vrikki, Photini Chapter 3 From Big to Democratic Data |
| title | Chapter 3 From Big to Democratic Data |
| title_full | Chapter 3 From Big to Democratic Data |
| title_fullStr | Chapter 3 From Big to Democratic Data |
| title_full_unstemmed | Chapter 3 From Big to Democratic Data |
| title_short | Chapter 3 From Big to Democratic Data |
| title_sort | chapter 3 from big to democratic data |
| topic | critical data practice, data as a public good, data solidarity, democratic data, data governance |
| topic_facet | critical data practice, data as a public good, data solidarity, democratic data, data governance |
| url | https://library.oapen.org/handle/20.500.12657/57277 |
| work_keys_str_mv | AT bunzmercedes chapter3frombigtodemocraticdata AT vrikkiphotini chapter3frombigtodemocraticdata |