=Paper=
{{Paper
|id=Vol-3908/paper_61
|storemode=property
|title=Risky Complaints: Unpacking Recent Trends in Risk Assessment Across Global Supply
Chains
|pdfUrl=https://ceur-ws.org/Vol-3908/paper_61.pdf
|volume=Vol-3908
|authors=Gabriel Grill
|dblpUrl=https://dblp.org/rec/conf/ewaf/Grill24
}}
==Risky Complaints: Unpacking Recent Trends in Risk Assessment Across Global Supply
Chains==
https://ceur-ws.org/Vol-3908/paper_61.pdf
Risky Complaints: Unpacking Recent Trends in Risk
Assessment Across Global Supply Chains
Gabriel Grill1
1
University of Michigan, Ann Arbor
Abstract
Recently, new AI supply chain risk management technologies promise to aid in the anticipation and
reaction to potential disruptions. They rely on new data sources such as social media data and online news.
Among the phenomena marked risky are also so-called political risks such as local protests, labor strikes,
and other forms of unrest. These systems promise to better inform investment and mitigate political and
labor risks to companies. Recently, also legal concerns make such technologies that promise visibility
more appealing to companies as new regulations in the EU and beyond require greater oversight to curb
human rights abuses and environmental damage across supply chains. Concerningly, this technology
also potentially undermines worker voice and labor action as it can be used to make their impacts felt less
by companies with more control over supply chain operations. In this paper, I use situational analysis to
critically unpack descriptions and broader discourse in public materials of data and algorithms potentially
used within such systems. I problematize emerging risk assessment logics in supply chain management
and discuss political issues this technology poses to protest as a form of democratic participation.
Keywords
Digital Supply Chain, Risk Management, Labor Surveillance, Analytics, Science and Technology Studies
1. Introduction
In the last years, concerns about the resilience of global supply chains have increased after many
felt disruptions in their everyday lives. This led to established regimes that focused foremost on
optimizing speed and cost, e.g., via offshoring and just-in-time management, to be questioned.
As noted by an industry magazine, “supply chain disruptions are increasingly unavoidable”
[1], and thus, companies should consider shortening their supply chains and creating more
transparency. In addition, new regulations across the globe are mandating companies to improve
supply chain transparency due to concerns about sustainability and human rights. For example,
a recent law in Germany increased requirements for companies to identify and mitigate human
rights abuses and negative environmental impacts. Similarly, in the US, the Uyghur Forced
Labor Prevention Law requires US importers to prove that goods were not produced with the
involvement of forced labor [1].
These calls for visibility and resilience also partially align with promises made by advocates
of digital supply chains, which also promote AI products for risk management. These new tools
EWAF’24: European Workshop on Algorithmic Fairness, July 01–03, 2024, Mainz, Germany
$ ggrill@umich.edu (G. Grill)
https://ggrill.net/ (G. Grill)
0000-0002-4879-0553 (G. Grill)
© 2024 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
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promise to aid in the anticipation and reaction to potential disruptions and increase transparency
across global supply chains. Among the phenomena identified by them are so-called political
or human-made risks such as labor strikes, human rights abuses, and unrest [2]. In this short
paper, I present preliminary insights based on research on risk management AI tools that target
protests and strikes. I understand them as a form of Unrest Detection and Prediction technology
(UDP). These systems promise to inform investment and to mitigate political and labor risks
by minimizing impacts of disruptions, e.g., by reactively changing suppliers and aiding in the
avoidance of reputational damage, e.g., when labor disputes may point to problematic working
conditions [3]. Concerningly, this technology potentially undermines worker voice and labor
action as it also often seeks to lessen their impacts on companies with more control over global
supply chains.
I aim with this work to unpack emerging risk categorizations in supply chain management as
new AI tools are adopted and tried out. I discuss their implications and point to possibilities for
different supply chain futures. In my analysis, I understand these supply chain risk management
tools as classification infrastructures [4] in modern globalized supply chain capitalism [5, 6] that
enable surveillance practices meant to control and manage workers and suppliers. I argue they
should be seen as technofixes [7, 8] often meant to close debates on problematized practices
through performances of compliance without meaningfully addressing underlying structural
inequalities. These tools thereby both potentially enable problematic surveillance and also
normalize it. My analysis ultimately problematizes these risk assessment tools in supply chain
management, points to challenges in regulation to improve transparency and accountability of
supply chain risk management, and discusses issues this technology poses to protest as a form
of democratic participation.
2. Methods
I use situational analysis [9] to critically unpack data and algorithms potentially used within
such systems, as well as related discourses that frame the technology as an integral part of the
future of supply chain management. My analysis is mostly based on public documents such
as research papers, videos, web pages, reports, and advertisements. My focus is on materials
produced by companies and researchers in positions to shape and use these systems, with the
aim of “studying up” [10] and critically examining established practices and assumptions. I
understand these documents as created with certain audiences and conventions in mind [11],
and thereby treat them not as mere factual statements but as human-made and imbued with
priorities that can give important insight into related discourses and practices but don’t reveal
absolute, underlying truths. Since some of these practices are treated as trade secrets, only
limited public information is available, which further increases the difficulty of conducting this
research. This also holds for more detailed information about surveillance practices targeting
social movements, which tend to be considered risky. They often become known to wider
audiences through investigative journalism and leaks, e.g., how Amazon is developing social
media surveillance tools to watch workers [12]. These challenges pose limitations to this
research.
3. Unpacking Algorithmic Risk Assessment
The great availability of all kinds of data on the internet, such as online news articles, blog
posts, and social media posts, has made the collection of so-called “open source intelligence” [2]
more feasible. It has motivated a shift in corporate intelligence and knowledge practices toward
increasingly considering also big, alternative, and real-time data [13]. It can be understood as a
continuation of old myths of calculability in risk management [14] and also as reinvigorating
them through the promise of big online data [15].
An example of a provider of such risk analytics suites is the Austrian company Prewave [16].
They promise to aid analysts in anticipating all kinds of risks that can disrupt supply chains
and, more recently, also compliance with new supply chain laws that demand more visibility.
Their tools are based on publicly available data like social media posts, which resulted in them
promoting their product at some point as a “shitstorm insurance” [16] able to anticipate emerging
grievances. Beyond the detection capabilities of Prewave, some machine learning-powered tools
in this space promise sophisticated analysis of social media posts. For example, the Australian
company Fivecast promises to detect affective language and emerging social networks, which
are presented as indicative of forming unrest. Its tool also offers to recognize objects/faces
in pictures posted on social media to enable search at scale [3]. These new approaches both
introduce new regimes of managing risk and also continue old logics.
These AI tools tend to situate risk in entities like areas, suppliers, and workers [3] perceived as
interchangeable or controllable instead of mainly examining structural issues like exploitation
or insufficient payment. They construct risk from the perspective of a top-tier company in the
supply chain, which is mainly concerned with minimizing reputational risk and disruptions to
its operations. Thus, these risk categorizations tend to highlight priorities of companies [14].
They have also been advertised as tools to enable the detection of human rights abuses, early
communication with protesters before disruptions, and understanding grievances in real-time
[2]. However, the effectiveness of such an approach is questionable, as discussed in more detail
in prior work [2]. Current incentive structures optimizing efficiency and cost-cutting encourage
using such tools as technofixes to curb controversy and continue shifting risk down in the supply
chain to marginalized people [17], which can have devastating consequences. For example, areas
could be marked as risky due to a history of strong labor movements, which could incentivize
divestment and thereby hurt regions [3]. Similarly, suppliers could be marked as risky when
workers are in the process of unionizing, which could motivate top-tier companies to replace
them to avoid disruption, thereby potentially destroying unionized labor [3].
Since some UDPs use machine learning techniques, such as sentiment analysis, to identify
and mark concerns voiced online by workers as indicators of potential risk, they also introduce
new logics. They construct risk based on behaviorist ideas [18] since affect, complaint, and
collective action are marked as risky. These assumptions also introduce new errors [2] that may
negatively impact workers. Various prior studies have highlighted how social media data is
limited, messy, and unrepresentative of what’s happening offline [19, 20]. Prominent incidents
like the Google Flu failure highlight severe limitations of such technologies based on online
data in practice and at scale, [21]. This is an industry-wide problem as many prominent AI
products have been called out for misrepresenting capabilities and their tendencies to reinforce
inequalities and marginalization [22, 23, 24]. Producing more risk ascriptions at scale through
such tools may only further animate suspicion and cause harm and problems to various actors
within supply chain networks.
4. Conclusion
The potential for UDPs to be used against worker interests and concerns around misrepre-
sentation puts into question recent efforts by companies to brand UDPs as tools for supply
chain transparency to further worker rights as demanded by recent regulations. For instance,
Prewave, which states on its homepage that its supply chain risk management products are used
by big companies such as BMW and Fujitsu, presents its technology as a way to address new
requirements of the German Supply Chain Act [25, 26]. These new regulations may thereby
only further normalize new modes of digital surveillance in risk management while discretion
of how they are used still largely remains with companies and management personnel. More
surveillance technology will not address the underlying issues; it will require political and
structural changes.
The many recent supply chain crises destabilize established practices and, therefore, could
also be a moment to rethink aspects of the current global supply chain regime [27, 6]. It could
be an opportunity for more democratic participation to engage questions about what kinds of
supply chains would be desirable and how globalized economies should be organized. Modern
supply chains tend to be structurally opaque [28] and hidden as infrastructure in the background
[4, 29], but the recent crises bring them to the forefront and channel attention, making them
pressing topics that can be discussed more widely. This could aid social movements that have
tried to change supply chain regimes for decades and make hidden exploitation of people and
planetary resources more visible. This requires organizations, companies, and decision-makers
to strive to understand and meet grievances and complaints instead of framing them as mere
risks to be averted. A change toward supply chains built on justice, sustainability, and trust
will require political pressure and the building of coalitions. This entails worker organizing and
solidarity on a global level across supply chains to foster understanding and developing supply
chain standards centering worker rights.
References
[1] Pia Vining, Coping With the ‘New Normal’ for Supply Chain Due Diligence, Supply Chain
Brain (2022).
[2] G. Grill, Future protest made risky: Examining social media based civil unrest prediction
research and products, Computer Supported Cooperative Work (CSCW) (2021) 1–29.
[3] G. Grill, C. Sandvig, Military AI’s Next Frontier: Your Work Computer, Wired (2023).
https://www.wired.com/story/military-ais-next-frontier-your-work-computer/.
[4] G. C. Bowker, S. L. Star, Sorting Things out : Classification and Its Consequences, MIT
Press, 1999.
[5] M. Murphy, The Economization of Life, Duke University Press, 2017. doi:10.1215/
9780822373216.
[6] A. Tsing, Supply Chains and the Human Condition, Rethinking Marxism 21 (2009) 148–176.
doi:10.1080/08935690902743088.
[7] E. Morozov, To Save Everything, Click Here: The Folly of Technological Solutionism, first
edition ed., PublicAffairs, New York, 2013.
[8] S. Lindtner, S. Bardzell, J. Bardzell, Reconstituting the Utopian Vision of Making: HCI After
Technosolutionism, in: Proceedings of the 2016 CHI Conference on Human Factors in
Computing Systems, CHI ’16, Association for Computing Machinery, San Jose, California,
USA, 2016, pp. 1390–1402. doi:10.1145/2858036.2858506.
[9] A. E. Clarke, C. Friese, R. S. Washburn, Situational Analysis: Grounded Theory after the
Interpretive Turn, Sage Publications, 2017.
[10] G. E. Marcus, M. M. Fischer, Anthropology as Cultural Critique: An Experimental Moment
in the Human Sciences, University of Chicago Press, 2014.
[11] G. A. Bowen, Document Analysis as a Qualitative Research Method, Qualitative Research
Journal 9 (2009) 27–40. doi:10.3316/QRJ0902027.
[12] L. K. Gurley, Secret Amazon Reports Expose the Company’s Surveillance of Labor and
Environmental Groups, Vice (2020).
[13] K. B. Hansen, C. Borch, Alternative data and sentiment analysis: Prospecting non-standard
data in machine learning-driven finance, Big Data & Society 9 (2022) 20539517211070701.
doi:10.1177/20539517211070701.
[14] D. Lupton, Risk: Second Edition, Routledge, 2013. doi:10.4324/9780203070161.
[15] J. Van Dijck, Datafication, dataism and dataveillance: Big Data between scientific paradigm
and ideology, Surveillance & Society 12 (2014) 197–208.
[16] G. Grill, RESEARCHING ONLINE LABOR STRIKE AND PROTEST PREDICTION TECH-
NOLOGIES, AoIR Selected Papers of Internet Research (2020).
[17] P. Moradi, K. Levy, The Future of Work in the Age of AI: Displacement or Risk-Shifting?,
Oxford University Press, 2020, pp. 269–288. doi:10.1093/oxfordhb/9780190067397.
013.17.
[18] L. Stark, J. Hutson, Physiognomic artificial intelligence, Fordham Intell. Prop. Media &
Ent. LJ 32 (2021) 922.
[19] E. Hargittai, Is bigger always better? Potential biases of big data derived from social
network sites, The ANNALS of the American Academy of Political and Social Science 659
(2015) 63–76.
[20] Z. Tufekci, Big questions for social media big data: Representativeness, validity and other
methodological pitfalls, in: Proceedings of the international AAAI conference on web and
social media, volume 8, 2014, pp. 505–514.
[21] D. Lazer, R. Kennedy, G. King, A. Vespignani, The parable of Google Flu: Traps in big data
analysis, Science 343 (2014) 1203–1205.
[22] R. Benjamin, Race after Technology: Abolitionist Tools for the New Jim Code, John Wiley
& Sons, 2019.
[23] G. Grill, Constructing Capabilities: The Politics of Testing Infrastructures for Generative
AI, in: Proceedings of the 2024 ACM Conference on Fairness, Accountability, and Trans-
parency, FAccT ’24, Association for Computing Machinery, New York, NY, USA, 2024, pp.
1838–1849. doi:10.1145/3630106.3659009.
[24] A. Narayanan, S. Kapoor, AI Snake Oil: What Artificial Intelligence Can Do, What It
Can’t, and How to Tell the Difference, Princeton University Press, 2024. doi:10.1515/
9780691249643.
[25] Prewave, Supply chain act Germany, 2022. https://www.prewave.com/
supply-chain-act-germany/.
[26] L. D. Klausner, M. Heimstädt, L. Dobusch, »Schöne neue Lieferkettenwelt«: Workers’ Voice
und Arbeitsstandards in Zeiten algorithmischer Vorhersage, in: T. Haipeter, M. Helfen,
A. Kirsch, S. Rosenbohm (Eds.), Soziale Standards in globalen Lieferketten, transcript
Verlag, 2023, pp. 97–114. doi:10.1515/9783839467701-005.
[27] I. Burrington, After Supply Chain Capitalism, 2020. https://points.datasociety.net/
after-supply-chain-capitalism-bca0d5ce2ae1.
[28] M. Posner, Breakpoints and black boxes: Information in global supply chains, Postmodern
Culture 31 (2021).
[29] M. C. Hockenberry, N. Starosielski, S. M. Zieger (Eds.), Assembly Codes: The Logistics of
Media, Duke University Press, Durham, 2021.