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Resisting the Algorithmic Boss: Guessing, Gaming, Reframing and Contesting Rules in App-based Management

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Abstract

Digital labour platforms do not appear as fertile ground for collective opposition. Precarious working conditions, digital control and anonymity are believed to impede resistance. However, research has shown that algorithmic management can create new conditions, spaces and practices of resistance. Our study set out to investigate the relationship between app-based management and collective opposition in the case Deliveroo and Foodora food-delivery workers in Berlin. We found out that being a subject of app-based management amplifies the insecurity and instability of already precarious gig-work. Information vacuum, lack of feedback mechanism and data-driven performance control are the three core elements of 'digital precarity'. These conditions fuel the need for practices of collective learning, gaming, reframing and contesting the algorithmic system. The paper emphasizes that many practices hidden from the public eye could be considered resistance, because they expose and challenge the power imbalance between the workers and the platforms.
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Resisting the Algorithmic Boss:
Guessing, Gaming, Reframing and Contesting Rules in App-based Management
Joanna Bronowicka
Center for Interdisciplinary Labour Law Studies
European University Viadrina, Germany
joanna@cihr.eu
Mirela Ivanova
Seminar für Soziologie
University of Basel, Switzerland
mirela.ivanova@unibas.ch
Abstract
Digital labour platforms do not appear as fertile ground for collective opposition. Precarious working
conditions, digital control and anonymity are believed to impede resistance. However, research has
shown that algorithmic management can create new conditions, spaces and practices of resistance.
Our study set out to investigate the relationship between app-based management and collective
opposition in the case Deliveroo and Foodora food-delivery workers in Berlin. We found out that
being a subject of app-based management amplifies the insecurity and instability of already
precarious gig-work. Information vacuum, lack of feedback mechanism and data-driven
performance control are the three core elements of ‘digital precarity’. These conditions fuel the need
for practices of collective learning, gaming, reframing and contesting the algorithmic system. The
paper emphasizes that many practices hidden from the public eye could be considered resistance,
because they expose and challenge the power imbalance between the workers and the platforms.
RESISTING THE ALGORITHMIC BOSS
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Resisting the Algorithmic Boss:
Guessing, Gaming, Reframing and Contesting Rules in App-based Management
Don’t believe the hype - the new fee system is a trap! Come together now!
Invitation to Deliverunion meeting in Berlin1
In December 2018 a group of over 30 food-delivery couriers, activists and researchers gathered
in Berlin to discuss the new payment system implemented by Deliveroo. Instead of paying the same
fee for each delivery, the company would price each order differently based on the distance
between the restaurant and the customer. The new algorithmic rule used was not revealed to the
workers, so the riders active in the FAU union organized a meeting to share information they have
gathered thus far. First, riders from France and UK explained over Skype how the new scheme
deteriorated their working conditions. Then, a rider presented how he ‘reverse engineered’ the
algorithm to approximate the formula used to calculate the new pay rate.
As riders speculated about the algorithm, we asked ourselves should this type of guessing
count as collective resistance? What conditions made this form of resistance necessary and
possible? Drawing on the case study of Deliveroo and Foodora riders in Berlin, we decided to look
beyond the public collective actions such as protests or strikes, and focus on the hidden practices of
resistance to the algorithmic management. Between March 2018 and January 2019, we conducted
semi-structured interviews with 20 riders and six company representatives. Although the two
companies used different employment models - Deliveroo riders in Berlin were self-employed while
Foodora riders were employees - their digital strategies to control workers’ autonomy were strikingly
similar.
We also carried out observations of rider interactions at work, after hours, or during union
meetings. We attempted to capture the conditions of opposition to the algorithmic management, the
reasons behind it and the moments it was most likely to occur. We selected participants using the
‘chain referral sapling method’ and tried to ensure that respondents “reflect what are thought to be
the general characteristics of the population in question” (Biernacki & Waldorf, 1981, p. 155). Our
sample was quite heterogeneous: it contained both union members and riders who are critical
towards unions, passionate bikers and those who dislike riding, part-time and full-time workers,
migrants and German nationals, men and women.
We found that being subject of algorithmic management adds a layer of ‘digital’ to an already
precarious condition in other words, the sense of insecurity is only deepened by digital
characteristics such as information vacuum, lack of feedback mechanism and data-driven
!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
1 Deliverunion was a joint initiative of Foodora and Deliveroo riders supported by the FAU union in Berlin:
https://deliverunion.fau.org/2018/11/27/dont-believe-the-hype-the-new-fee-system-is-a-trap-come-together-now/#more-
432
RESISTING THE ALGORITHMIC BOSS
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performance control. We discovered that many collective practices in an app-based management
concern guessing the algorithmic rules and bypassing them, but also reframing them in a way that
they can be contested. We conclude that practices hidden from the public eye could be considered
resistance when they expose and challenge the power imbalance between the workers and the
platforms.
Algorithmic Management and Resistance
Practices of contestation need to be analytically located within a ‘terrain’ of a specific
management regime (Edwards, 1979), as different models generate “their own contradictions and
conditions” for resistance (Ackroyd & Thompson, 2016, p. 188). The model of digital labour
platforms aims to save labour costs by relying on workforce made up of independent contractors or,
rarely, short-term employees (Srnicek, 2017), while high turnover and poor bargaining power
(Vandaele, 2018) add to the precarious working conditions. The particularity of the management
regime of platforms relies on the technical ability to control the labour process of their workers
through “a distinctive, digital-based ‘point of production’(Gandini, 2019, p. 1044) - in this case the
mobile app which embodies ‘the rules of the game’ continuously updated in “a constant process of
innovation and experimentation” (Ivanova, Bronowicka, Kocher & Degner, 2018, p.10).
At a first glance app-based work does not seem to be a fertile ground for oppositional practices.
Payment per gig, data-driven control, high turnover and physical isolation prevent the emergence of
shared experiences, understandings and norms (Graham & Woodcock, 2018). However,
researchers have documented a wide variety of resistance practices in algorithmic workplaces, from
protests and strikes, to efforts to build work councils, providing evidence that insurrection against
the power of platforms is indeed feasible (Animento, Di Cesare & Sica 2017; Ecker, Le Bon &
Emrich, 2018; Degner & Kocher, 2018; Chen, 2018; Vandeale, 2018; Herr, 2017). Moreover, a
survey of recent studies on crowd- and gig- workplaces reveals the importance of creating spaces
for in-group expression outside of the platform boundaries, such as online forums or social media
(Yin et al., 2016; Rosenbalt & Stark, 2016; Wood, Lehdonvirta & Graham, 2018). In contrast to
public resistance practices, such as protests or strikes, the nature of individual and collective hidden
practices is more ambiguous and sometimes dismissed as politically insignificant (Contu, 2008).
Yet, Mumby, Thomas, Martí and Seidl (2017) propose that both public and hidden practices, called
‘infrapolitics’, can indeed be considered resistance when the “prevailing structures of power are
made visible, denaturalized, and the metrics for their operation is placed under scrutiny and
questioned” (p. 1164).
In the context of digital platforms, ‘collective infrapolitics’ can reveal information and power
asymmetries between the platforms and the workersfor example by ‘guessing’ the opaque
algorithms, which translate workers’ performance into data used to monitor and evaluate them
(Möhlmann & Zalmanson, 2017). In order to avoid the disciplinary grip of algorithmic management,
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Lyft and Uber drivers shared their mistakes and discoveries with others, pooled together their
observations and generated hypotheses about the rules of fare pricing or distribution (Allen-
Robertson, 2017). Through this collective process, workers construct theories, stories and urban
legends (Möhlmann & Zalmanson, 2017), the ‘algorithmic imaginary’ (Chan & Humphreys, 2018) or
the ‘allegorithm’ (Anderson, 2016). Collective practices can then turn to ‘gaming’ the system or
bypassing the rules of algorithmic management - Uber drivers deactivate the GPS to avoid
punishment for rejecting unprofitable rides (Chan & Humphreys, 2018), while Didi drivers use
different bot apps to catch the highest fares (Chen, 2018). As the workers develop an understanding
that the game is rigged over time, the ‘allegorithm’ fades and imaginaries can be deconstructed. A
moral sense that algorithmic management is overly opaque and unfair makes workers suspect
(Shapiro, 2018; Möhlmann & Zalmanson, 2017). In reaction to this disenchantment, some of them
might choose ‘voice’ as a strategy by constructing new frames and stirring change from within
(Hirschman, 1970).
Digital aspects of algorithmic management cannot be easily separated from other important
circumstances such as payment per gig, high staff turnover or absence of union structures. In our
findings, we turned our focus to these conditions and practices of resistance, which do appear
unique to app-based management or, more broadly, digitally-mediated labour.
Findings
Interviews with the Foodora and Deliveroo riders revealed three ‘digital’ elements, which add to
the already precarious working conditions information vacuum, lack of feedback mechanisms, and
data-driven performance control.
The first condition of ‘digital precariat’ is the shared experience of working in an information
vacuum and communicative isolation from other workers. Riders described “being left alone with
your questions” as a permanent condition perpetuated by frequent changes of the software design
or rules “happening in an almost weekly rhythm, which makes you feel like a guinea pig.They
reported feeling “confused”, “paranoid”, “afraid of getting fired…because you don't really get to
understand all of the statistics,” and compelled to learn rules behind the algorithm, because “it is so
omnipresent; you need to deal with it all the time.” Yet, their attempts were frustrated by lack of
designated physical or digital spaces for worker interaction, described as deliberate attempts “to
prevent us from talking to each other”.
Riders were also isolated from company representatives and managers. Attempts to reach
them by email were frustrating as “it can take two three days working days to get a reply, but then
you get a reply and you're never satisfied because they don't explain so much.” One rider described
the experience of being a voiceless worker by comparing emailing the office to “shouting in the
forest and hoping somebody is hearing.” Lack of voice or representation structures for workers
prompted them to look for alternative means of collectively addressing their needs.
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Lastly, the experience of being subject of data-driven performance evaluation also exacerbated
the shared feeling of insecurity. Only the riders with the best statistics were given access to good
shifts, while the others were left to compete for the remaining ones (Ivanova et al., 2018). For the
riders with poorer statistics access to profitable time slots and city zones was limited leaving them
feeling “more insecure than ever.” The way the statistics were compiled was also deemed “unfair”
and “ruthless,” since it did not account for such reasons for being late or absent as illness, accident,
broken bike or no internet access.
These shared conditions of app-based management gave rise to a collective experience of
‘digital precarity’: confusion, isolation, insecurity and lack of voice, but also fuelled the need to
create new digital spaces for collective experiences and processes outside of the organisational
boundaries. These self-organized communicative structures on WhatsApp, Slack or Facebook
provided diverse opportunities for in-group identification and generated new collective strategies to
share information, help each other, express voice and, ultimately, change the power relations at
work.
Guessing the Algorithm - Collective Rule-discovery Practices
We found that Deliveroo and Foodora riders filled the information vacuum created by the app-
based management by engaging in collective rule discovery. They collected information on their
own and shared it with others, thus translating individual hypotheses into collective theories. These
collective practices of guessing the rules of the game often had the aim of regaining control over the
working process and challenging the legitimacy of the management regime.
Much of the communication in person or digitally was dedicated to “guessing and gossiping”
about the unknown rules of the app-based management. As a rider explained, “of course, we all
always speak about these things we don't know with my colleagues.” Our observations and
interviews revealed that riders regularly tried to guess how the algorithm assigning orders works.
They questioned if it is really only the GPS location and the proximity to the restaurant that is taken
into account or if other perimeters are playing a role too. Deliveroo riders were similarly perplexed
about how the metrics of the shift sorting system work. While they knew that being late and missing
a shift were the two criteria determining their position in the badge system, it was unclear how many
times they should come on time in order to improve their standing. . The practices of sharing
experiential knowledge seemed especially valuable when one has just started the job. For
example, riders reassured a new colleague that the notification : ‘Your acceptance rate is pending’
is nothing to worry about : “I have been refusing 4 or 5 [orders] in a row and it was ok"
Practices of collective learning intensified when app changes were introduced, because riders
were often not warned about new rules and left to discover them alone. For example, when the
platforms introduced a feature that would reveal the riders name to the customer, the news was
shared by word of mouth. Major design overhauls prompted the riders to share the results of their
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trial and error experiments with others and discover how the app-based management actually
works. For example, when Foodora introduced a new shift-booking system a rider learned that “you
only find out by playing basically. You need to game around or ask the people to game around.”
We discovered that the guessing contributed to construction of a collective imaginary about the
algorithmic rule. For example, when it comes to the algorithm allocating orders, some riders
believed that the algorithm is fair and equalizes the number of orders per shift among the riders,
while others believed that “the faster you are, the longer distance orders you will get.” These
imaginaries had behavioural consequences some riders cycled slower to avoid being assigned
long distance gigs or rode around the restaurant in hope of getting the next order sooner.
The intention to share useful information rather ‘free-ride’ on the information collected by others
points to political nature of this practice (Olson, 1965, Jasper, 2011). As some authors suggest, rule
discovery and guessing the system “often describe malicious attempts by a platform uninterested in
drivers’ wellbeing and success and encourage drivers’ action and resistance” (Möhlmann &
Zalmanson 2017, p. 11). Indeed, bringing clarity to deliberately obscure rule design can be
considered resistance not if it’s done for personal gain, but when the goal is to expose unfair rules
in a way that they can be bypassed or challenged, and tip the balance of power in favour of
workers.
Gaming the System Bypassing the Algorithmic Rules
Collective strategies practiced by Deliveroo and Foodora riders went beyond learning the rules
their goal was to ‘game’ them and avoid punishment by the platform. For example, riders
downloaded apps to spoof their GPS location to cover up being late for a shift and protect their
statistics. Also, they avoided penalty for missing a shift by staying at home and rejecting all
incoming orders. Rather than keeping these strategies private, riders eagerly shared information
how to ‘game the system’ by exploiting app design flaws and opportunities for misbehaviour.
Without the physical boss, these practices were easier, because, as one rider described, “The app
doesn’t see everything…so you can [pretend] that you are behaving like the company expects you
to behave, but in fact, you are not.”
Our research suggests that the line between individual and collective gaming strategies is
blurry, since individual misbehaviour can contribute to collective solidarity. When Deliveroo
introduced a shift-booking system automatically sorting riders according to their statistics, the riders
reacted by creating a “black-market for shifts”. In defiance of a competition logic established by the
company, riders with better statistics found a way to reassign profitable shifts to those with worse
statistics for no apparent personal gain. The political intent of this practice was clear it exposed
how the data-driven ranking system is unfair, because it can cost riders with lower statistics to lose
their jobs. The shift-exchange practice is an example of ‘digital misbehavior’ or ‘algorithmic activism”
(Chen, 2018) of platform workers who exploit the vulnerabilities of an app-based management
RESISTING THE ALGORITHMIC BOSS
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system. It also shows that the shared experience of precarity in the gig economy can lead to the
creation of informal networks of support and foster collective opposition.
We conclude that the shift exchange practice, although invisible to the company and
bystanders, should count as resistance, because it exposes the logic of control though competition
and replaces it with logic of solidarity. More broadly, we suggest that gaming strategies can be
considered a form of resistance to algorithmic rules, when they spark the collective questioning of
their social design.
Reframing the Job Hidden Expressions of Dissent
We discovered that the same communicative structures used for sharing information about
working rules could be quickly repurposed as spaces for collective expressions of grievances with
the management. As riders reported,” this was always a topic - the conditions and how Deliveroo is
not doing good to the employees.” We observed that even when riders met to relax after work, the
conversation quickly turned to sharing frustrations about the working conditions. The interviews also
revealed grievances about the app-based management: mistakes in the data collection and the lack
of clarity about how statistics are used for automated sorting. Perplexed with his average speed of
38 km/hour, one rider wondered: “The algorithm has calculated something but apparently there
must be kind of a mistake… you know, sometimes they just have wrong interpretations of the data
or it leads to kind of wrong results.”
We also observed that when a company introduced sudden and unilateral changes to the
management regime, the expressions of dissent intensified into a collective ‘moral shock’ (Jasper,
2011). This is how a rider described a reaction on a WhatsApp group on the day the new shift
system was introduced: “There was a chat of, I think, a thousand lines. Everybody was going like
What the fuck are you doing? You fucking idiots! ... It was a serious ‘fuck you’ moment.” These
moral shocks played an important role in worker mobilisation for collective action because they
made it apparent that the management practices do not align with their own moral frames.
The subsequent reframing of the app-based management regime by the workers took place in
spaces outside of the organizational boundaries and away from the supervisors’ gaze. The app-
based management regimes appear to have different affective politics than traditional workplaces,
where management can simulate and standardize expressions of affect in order to “prevents
workers from establishing more traditional friendship and community networks” (Gregg, 2010, p.
253). The workspace of Deliveroo and Foodora riders seemed free of such affective norms, which
they considered an advantage of this type of work: “here you don’t have to smile and make
everything look nice.” We suppose that absence of affective control can open new space for
“affective solidarity” (Moore, 2019), as well as collective expressions of dissent.
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Contesting the Algorithm Collective Expressions of Voice
In addition to the hidden and ambiguous practices of guessing, gaming and reframing the
working rules, we also observed that workers expressed their ‘voice’ publicly either directly with
the company management or through a union. The first strategy involved sharing grievances by
sending management representatives emails, letters and messages on Slack or addressing them
directly in meetings. For example, when Deliveroo changed the criteria for receiving a bonus, a
group of riders wrote a letter stating: “This is definitely not correct and you can't just take away
something that we are relying on so much." Deliveroo riders requested a meeting to propose an
alternative shift-booking system, which would allow for exchanging shifts between the riders. They
also invited company to join Slack platform to be able to share their feedback with the company, but
also to increase their visibility or as one rider put it, to let them know that “we are here.” The
followers of this first strategy believed that individual or collective expressions of voice had positive
impact on management decisions.
In contrast, the members of the Deliverunion campaign unionised in FAU assumed that the
management is not likely to implement desirable changes unless pressured by the workers or the
general public. The Deliveroo and Foodora riders who joined the campaign have succeeded in
raising public awareness about the working conditions in food-delivery platforms through petitions,
demonstrations and protests. In January 2018, Foodora riders protested by ‘delivering’ broken bike
parts in front of the Berlin office of the parent company Delivery Hero. In April 2018, Deliveroo riders
‘delivered’ a pizza box containing signatures of 150 riders demanding better working conditions to
the Berlin office. In addition to these actions reported by the media, the Deliveroo riders also
organized a ‘log-off strike’ in one of the city zones, but felt short of full rider participation to achieve a
political impact.
The unexpected changes to digital conditions presented union organisers with opportunities for
collective mobilization. As a Foodora union member explained, the decision to reveal the rider name
to the restaurant and the customer catalysed the Deliverunion campaign: “that was actually the
moment we started organizing ourselves.” The union demands evolved with time, but at first they
focused on the working conditions coverage of the bike repair costs, higher wages, and a
guarantee of enough working hours per week. Later, the demands were more specific to the
conditions of the app-based management for example Deliveroo riders asked for better
transparency about their working hours, and Foodora riders requested to be paid one hour per week
for shift planning.
Public actions, such as protests, demonstrations or strikes, were expressions of collective voice
towards the company, but also towards general public of supporters of the worker movement. In the
platform economy data-driven architecture turns workers invisible, calculable and easily replaceable
(van Doorn, 2017), so publicly displaying ones’ presence, discontent and opinion is undoubtedly a
form of opposition. Ultimately, when workers fail “to gain access to the flows of domination in order
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to participate in the decisions that affect them” by engaging with the management directly (Fleming
& Spicer, 2007, p. 48), public resistance is seen as the only and last resort.
Discussion
The goal of our study was to understand the relationship between algorithmic management and
practices of resistance. We found that app workers engaged in a multitude of oppositional practices,
despite obstacles typical to gig-work and specific to algorithmic workplaces. The practices of
guessing, gaming, reframing and contesting the algorithmic rule were similar to what other scholars
have described in other studies about app-based workplaces. Our results contribute to this line of
inquiry by detailing the conditions of ‘digital precarity’ as well as the possibilities and forms of
‘algorithmic resistance’.
First, we conclude that algorithmic management creates conditions, which add to the feeling of
insecurity and instability resulting from the precarious employment model. These conditions are
communicative isolation and information vacuum, lack of voice and representation structures and
use of data for controlling workers rather than supporting them. This type of management can
certainly hinder organized resistance, but it can simultaneously foster shared experiences and
needs that can only be addressed by collective practices of learning, solidarity and resistance,
which undermine the algorithmic rule. As Cant (2019) recognised, while precarity increases
vulnerability it also sharpens class conflicts.
Second, we provide further examples of how workers can open new spaces for collective
resistance by exploiting technological vulnerabilities. We develop the notion of ‘algorithmic activism’
by showing new ways breaking or suspending the algorithmic rule (Chen, 2018). From spoofing
their GPS location in order to skip work without punishment, to exchanging profitable shifts between
each other in spirit of solidarity, workers can use their technical knowledge to distance themselves
from company’s disciplinary logic. Gig workers are quick to adopt digital technologies to their
struggles, as illustrated by the log-off strike which is an obvious response to the new model of work
termed as “logged labor” (Huws, 2016). The use of Slack shows that communication tools designed
for other workplaces can be easily repurposed even in an environment without physical bosses.
Each of these practices helps workers “create some space and autonomy in order to exercise a
degree of control” (Edwards, Collinson & Della Rocca, 1995, p. 284) not only as individuals, but also
as a group.
Finally, we conclude that hidden practices of algorithmic resistance can and should be counted
as resistance when they have a political intent. Guessing and gaming are often more than merely
‘coping strategies’ without any intent to change the underlying power structures (Sauder &
Espeland, 2009; Chan & Humphreys, 2018), but constitute resistance when they put the algorithmic
regime under scrutiny and question (Mumby et al., 2017). Indeed, the goal of collective rule
discovery is to uncover the unfair metrics and rules, and gaming helps to tip the balance of power in
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favour of the worker. When done collectively, gaming strategies can lead to reframing of the
information asymmetry as unjust and put access to information at the centre of workers’ demands.
In a well-designed game players gradually discover rules by trial and error. In the platform
economy, however, workers are not meant to fully understand the game rules and experimentation
is punished. These control mechanisms of the platform economy are in part a return to “industrial
systems” in the Western world (Cherry, 2016), but twisted in a new way. As algorithmic
management deepens the information asymmetries, traditional loci of struggle such as time, effort
and wages merge with new disputes over access to information and algorithmic fairness. The way
that workers, who are denied voice and representation by the platforms, practice novel forms of
resistance can provide with useful insights to the reconfigurations of power in the next era of
industrial relations.
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