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P113


Demystifying data supply chains: perspectives from markets of data sourcing, production, and brokerage 
Convenors:
Jamie Wong (Harvard University)
Wanheng Hu (Cornell University)
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Discussant:
Danah Boyd (Microsoft Research Georgetown University)
Format:
Traditional Open Panel

Short Abstract:

This panel seeks to investigate “data supply chains.” We invite contributions that help clarify the practices and techniques, and the assemblage of networks and channels – formal and informal, legal and illegal, regional and global – that enable the commodification and economization of digital data.

Long Abstract:

Data, like any commodity, do not come already commodified. While "raw data" may be an "oxymoron" (Gitelman 2013), the "rawness" of data is, nonetheless, relative to those who deal with digital data and the specific contexts where data are produced, traded, or consumed. To manufacture data as "commodity" and as "product" involves many stages, transformations, and negotiations, requiring skillful sourcing and combination of materials, quality control, and marketing. Important recent research has begun to unveil the hidden labors behind data-driven technologies and businesses (e.g. Gray and Suri 2019). Yet, little is understood about the configuration of networks and channels – formal and informal, legal and illegal – that enable the commodification and economization (Çalışkan and Callon 2009) of data. This panel seeks to further clarify the regional and global operations of “data supply chains” (Spanaki et al. 2018).

We invite papers that offer insights to ground speculative rhetorics and debates, especially those pertaining to the AI industry, about data and their value – economic or otherwise – using real-world examples. Possible perspectives include but are not limited to: What actors, practices, techniques, and technologies comprise the infrastructure necessary for data brokerage? How do data brokers make digital information fungible to be sold at set prices? What are the conventions of pricing data at different levels of “rawness,” and how do these vary between different domains and industries? What are the marketing practices and rhetorics around “valuable” data? How are understandings of data’s value construed differently along different stages of the “data supply chain”? What do real-world cases tell us about the “interoperability” of data (Ribes 2017) across different models and domains and its impact on the data market?

Accepted papers: