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- Convenors:
-
Matthew Archer
(Maastricht University)
Filipe Calvao (Graduate Institute of Geneva)
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- Discussant:
-
Nanna Thylstrup
- Format:
- Panel
- Sessions:
- Wednesday 8 June, -
Time zone: Europe/London
Short Abstract:
Supply chains are being reconfigured through big data and intelligent machines. Reflecting ethnographically on the growing role of artificial intelligence in newly digitized means of production, this panel explores the co-emergence of datafication and algorithmic governance in global supply chains.
Long Abstract:
Global supply chains are increasingly governed by "big data". From mining and agriculture to health and finance, corporations and other organizations are adopting artificial intelligence to enhance efficiency and capitalize on behavioral and predictive data for their operations. This suggests the emergence of new forms of supply chain governance, where the calculative agency of algorithmic systems creates new supply chain politics, forcing influential "lead firms" to grapple with newly empowered tech companies and creating spaces where traditional power dynamics are both resisted and reproduced, even as new supply chains are emerging to facilitate the movement of data and software.
In this panel, we are particularly interested in papers that attempt to theorize the epistemic politics of artificial intelligence in supply chain management, through ethnographic engagements with questions of transparency, traceability, accountability, and sustainability. These concerns bring together a growing interest in the anthropology of algorithms, data, and AI with more classic accounts of regimes of production, consumption, and exchange in economic anthropology. Some questions the panel seeks to address include:
Who controls the data and software that automated supply chain management processes depend on? What forms of resistance are emerging to contest these power dynamics?
As companies turn to remote operations and automated decision making, what kinds of work do these new supply chains and new forms of supply chain governance instigate, and what do they preclude?
What kinds of knowledge "count" as data that AI-driven automated management systems can interpret and act on, and what gets excluded or ignored?
Accepted papers:
Session 1 Wednesday 8 June, 2022, -Paper short abstract:
Supply chain risk management technologies promise through the combination of data sources and new algorithmic methods to aid in anticipating disruptions such as labor strikes. I unpack and problematize data and algorithms used within such systems and how they co-construct labor risks.
Paper long abstract:
Current supply chain risk management technologies promise through the combination of data sources and new algorithmic methods to aid in the anticipation and reaction to potential disruptions. Among the phenomena marked risky are also local protests, labor strikes, and other forms of unrest. These systems promise to curb labor risks to companies by either minimizing impacts of disruptions e.g. by reactively changing suppliers or aiding in the avoidance of reputational damage e.g. when labor disputes point to problematic working conditions. More recently also legal concerns make these technologies more appealing to companies as new regulations in the EU may require greater labor and transparency standards across supply chains. Concerningly, this technology also potentially undermines worker voice and labor action by making their impacts felt less by companies with more control over supply chain operations. In this paper, I unpack data and algorithms potentially used within such systems and related discourses by drawing mostly upon a situational analysis based on public documents, research papers, and other collected data. My analysis ultimately problematizes risk assessment regimes in supply chain management and points to how they also are involved in the production of certain forms of ignorance.
Paper short abstract:
Our research examines digital agriculture’s (in)accuracies and their repercussions. We argue that over-reliance on big data and algorithms can lead to ‘precision traps’: Decision-making in and about agriculture that is governed by the needs of AI.
Paper long abstract:
An essential assumption of the ‘digital revolution’ in agriculture is that big-data-driven technologies provide more accurate and precise information to farmers, allowing them to spend less time gathering and interpreting data on soil, animal and crop health. Based on the claim that big data can more accurately represent and plan the various risks and workflows of a farming operation, farmers are promised impartial decision-support. In practice, however, the relations between farmers, farmworkers and digital technologies turn out to be more complex, often with unanticipated consequences. Our research examines digital agriculture’s (in)accuracies and their repercussions. We argue that over-reliance on big data and algorithms can lead to ‘precision traps’ and further cement dependency on particular machinery and inputs. Farmers’ experience, keen observation and understanding of the partialities of algorithm-derived data and advice remain crucial, as does their socio-economic agency in making autonomous, informed and selective choices between technologies and datasets.