Abstract
AbstractIn this article, we examine the significance of establishing participatory and consentful recordkeeping practice in the face of ubiquitous use of records beyond their original intent. Among such secondary uses is the decontextualisation of data as part of the 'industrialisation' of access and use of ‘historical’ records within current transactional contexts, together with a wide range of data sharing practices arising from contemporary data science paradigms. To situate the call to action for consentful recordkeeping practice, we begin the article by exploring how human ability to navigate through the perpetual twilight of records becomes increasingly murky when a wholesale approach to data collection and governance is applied by machine learning practitioners. We then re-frame some classical archival principles to align them with participatory approaches; specifically, by expanding the scope of Jenkinsonian ‘moral defence’ as an imperative for proactive engagement with the Archival Multiverse. We then describe a case study of consentful recordkeeping in practice, using the example of the AiLECS Lab’s newly developed collection acquisition and management system. This principles-based framework informs our practices for collecting and curating datasets for machine learning research and development and aims to privilege the ongoing consent of those represented in records to their use. In the context of this work, our core premise is that technologies designed to prevent exploitation of children should aim to avoid underlying data practices that are themselves exploitative (of children or adults).
Funder
Westpac Safer Children, Safer Communities Impact Grant
Monash University
Publisher
Springer Science and Business Media LLC
Reference132 articles.
1. Agostinho D (2019) Archival encounters: rethinking access and care in digital colonial archives. Arch Sci 19:141–165. https://doi.org/10.1007/s10502-019-09312-0
2. Agostinho D (2021) Care. In: Thylstrup NB, Agostinho D, Ring A, et al (eds) Uncertain archives: critical keywords for big data. MIT Press Cambridge, Massachusetts, Cambridge, Massachusetts
3. Andreotta AJ, Kirkham N, Rizzi M (2021) AI, big data, and the future of consent. AI & Soc. https://doi.org/10.1007/s00146-021-01262-5
4. Angouri J, Glynos J (2009) Managing cultural difference and struggle in the context of the multinational corporate workplace: solution or symptom? IDA World
5. Attwood B (2008) In the age of testimony: the stolen generations narrative, “distance”, and public history. Publ Cult 20:75. https://doi.org/10.1215/08992363-2007-017