Modeling Ethics: Approaches to Data Creep in Higher Education.
Big data ethics
Data creep
Justice
Learning analytics
Predictive modeling
Universities
Journal
Science and engineering ethics
ISSN: 1471-5546
Titre abrégé: Sci Eng Ethics
Pays: England
ID NLM: 9516228
Informations de publication
Date de publication:
18 11 2021
18 11 2021
Historique:
received:
03
04
2021
accepted:
06
10
2021
entrez:
19
11
2021
pubmed:
20
11
2021
medline:
7
4
2022
Statut:
epublish
Résumé
Though rapid collection of big data is ubiquitous across domains, from industry settings to academic contexts, the ethics of big data collection and research are contested. A nexus of data ethics issues is the concept of creep, or repurposing of data for other applications or research beyond the conditions of original collection. Data creep has proven controversial and has prompted concerns about the scope of ethical oversight. Institutional review boards offer little guidance regarding big data, and problematic research can still meet ethical standards. While ethics seem concrete through institutional deployment, I frame ethics as produced. Informed by my ethnographic research at a large public university in the U.S., I explore ethics through two models: ethics as institutional procedures and ethics as acts and intentions. The university where I conducted fieldwork is the development grounds for a predictive model that uses student data to anticipate academic success. While students consent to data collection, the circumstances of consent and the degree to which they are informed are not so apparent, as many data are a product of creep. Drawing from interviews and participant observation with administrators, data scientists, developers, and students, I examine data ethics, from a larger institutional model to everyday enactments related to data creep. After demonstrating the limits of such models, I propose a remodeling of ethics that draws on recent works on data, justice, and refusal to pose generative questions for rethinking ethics in institutional contexts.
Identifiants
pubmed: 34796403
doi: 10.1007/s11948-021-00346-1
pii: 10.1007/s11948-021-00346-1
doi:
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
71Informations de copyright
© 2021. The Author(s), under exclusive licence to Springer Nature B.V.
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