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Journal ArticleDOI

Unfairness by Design? The Perceived Fairness of Digital Labor on Crowdworking Platforms

TL;DR: In this article, a qualitative survey among 203 US workers active on the microwork platform Amazon Mechanical Turk was conducted to analyze potential biases embedded in the institutional setting provided by on-demand crowdworking platforms and their effect on perceived workplace fairness.
Abstract: Based on a qualitative survey among 203 US workers active on the microwork platform Amazon Mechanical Turk, we analyze potential biases embedded in the institutional setting provided by on-demand crowdworking platforms and their effect on perceived workplace fairness. We explore the triadic relationship between employers, workers, and platform providers, focusing on the power of platform providers to design settings and processes that affect workers’ fairness perceptions. Our focus is on workers’ awareness of the new institutional setting, frames applied to the mediating platform, and a differentiated analysis of distinct fairness dimensions.

Summary (3 min read)

1. Introduction

  • Digital platforms, such as Uber, Airbnb, TaskRabbit, and Amazon Mechanical Turk, have brought disruptive change to many service industries.
  • The workforce engaged on these digital on-demand service platforms is often characterized by commodification, low cost, minimal institutionalization, and increasing anonymity.
  • These asymmetries are associated with variations in influence, autonomy, or "voice", which ultimately affect the perceived fairness of the labor facilitated by these platforms.

2.1 The emergence and design of digital labor

  • Digital and social technologies facilitate the emergence of new forms of digital labor, such as irregular unpaid forms of labor heavily relying on hedonic gratifications ('playbor') (e.g., Kücklich, 2005; ) , or remunerated crowdwork systems that rely on the distribution of work through open calls rather than assignment.
  • From the perspective of those requesting services, these new forms of digital labor have several advantages, such as efficiency through commodification and relative inexpensiveness given very low reservation wages (Aytes, 2013; Fish & Srinivasan, 2011; Kittur et al., 2008) .
  • Requesters choose screennames, beyond which workers receive little to no information on their identities or track records, whereas requesters may access metrics on the employment history of workers (Bergvall-Kåreborn & Howcroft, 2014) .
  • Workers can then browse and accept these tasks.
  • The platform offers only minimal mechanisms of recourse or conflict resolution in the case of a worker disagreeing with a rejection.

2.2 Fairness of labor

  • An instrumental perspective holds that fairness is important due to material or economic considerations.
  • Such a state may be inefficient and unstable.
  • From an interpersonal perspective, fairness contributes to the quality of social relations.
  • Fair treatment has been shown to improve employees' trust in their management, increase their job satisfaction, and enhance their intrinsic motivation.
  • The former describes the dignity and respect workers receive from others and the latter captures the level and quality of information and explanations as well as the accountability of authorities, as experienced in the workplace (Colquitt, 2001) .

2.3 Applying the fairness concept to digital microwork

  • Most explorations of workplace fairness have been conducted in the context of offline work relations.
  • The party benefiting from institutional biases may wish to improve the fairness of a transaction when considering the conditions that render the other party disadvantaged or vulnerable (Snyder, 2008) .
  • Employee voice also implies the ability to participate meaningfully in determining the terms of the employment relationship (Van Buren & Greenwood, 2008) .
  • The market dynamics, design, and processes of microtasking platforms may serve to increase the risk of exploitation, institutional biases, and limited employee voice.
  • The authors following empirical analysis of fairness perceptions in digital labor encompasses three elements.

3. Research design

  • The authors study aims to describe and understand microworkers' perceptions of working conditions on digital on-demand service platforms.
  • The authors recruited 203 participants, all of whom were experienced members and located in the United States.
  • The participants were then asked to elaborate on what exactly they considered to be unfair in these instances and to describe how they reacted to this perceived unfairness.
  • All comments were read thoroughly and independently multiple times by the members of the research team.
  • Each team member identified and listed recurring themes in the data and extracted a smaller subset of data representing textual units relevant to the salient phenomena (Wolcott, 1994) .

4.1 Workers' relationship with the platform

  • The authors find that workers' fairness perceptions are often initially shaped by their interactions with requesters, as most descriptions of perceived unfairness on AMT relate directly to requester behavior (e.g., unjustified rejection of work, lacking feedback, low pay).
  • At the same time, many respondents do not necessarily expect requesters to act fairly and behave responsibly toward their 'ultrashort-term employees' on their own.
  • Instead, they look to the mediating platform (1) to ensure sound transactional processes; (2) to prevent abusive behavior; and (3) to act as an arbitrator in cases of conflict.
  • To provide an overview of these complex relationships, the authors categorized the various role concepts ascribed to the platform by the interviewed workers based on their social valence as (1) positive, (2) negative, (3) mixed, (4) neutral, or (5) non-descript.

TABLE 1 Relationships toward the Platform ABOUT HERE

  • Among the positive role concepts, participants describe the platform as a 'friend in times of need', a 'benefactor', and an 'equal'.
  • The wide variety and colorfulness of negative attributes ascribed to the worker-platform relationship stands in contrast to the limited portion of users who actually report a poor relationship with the platform.
  • Not all relationship descriptions can be clearly ascribed positive or negative qualities.
  • Participants who describe neutral platform-relationships focus on a rational trade-off between the time and effort invested and the monetary gains or entertainment value received from their platform engagement.
  • Some participants also note that their relationship with the platform suffers from a lack of communication.

4.2 Perceived unfairness of digital labor

  • The authors find a number of common themes that emerge from the descriptions and examples provided by the participants.
  • These themes can be differentiated as addressing (1) the allocation of rewards and compensation (distributive fairness); (2) formal policies and procedures (procedural fairness); and (3) interpersonal treatment (interactional fairness), as displayed in table 2.

TABLE 2 Fairness Perceptions ABOUT HERE

  • Distributive fairness plays a prominent role in workers' fairness perceptions.
  • In such instances, workers criticize that although the platform allows requesters to withhold payment, workers do not command a similarly powerful instrument of sanction.
  • It could be hypothesized that workers who receive 'abysmal pay' and feel 'robbed' of their fair compensation not only find their work to be undervalued but also feel undervalued as a person.
  • Commonly, workers' displeasure turns against requesters and in some instances requesters are even accused of 'lying' or deceiving workers.
  • Finally, interactional fairness is also affected by platform features, such as the dispersion and anonymity of the digital workforce.

4.3 Suggestions for increasing platform fairness

  • Respondents share a number of propositions aimed at improving the perceived fairness of digital labor.
  • Many of these propositions apply to platform design features.
  • The authors find little evidence of critical reflection among workers on why the platform currently does not provide the desired features.
  • After all, some features demanded by workers to alleviate power imbalances and improve workplace fairness may conflict with the platform's interests.
  • Many suggested features or solutions touch upon the basic quality of the work conducted on digital platforms.

TABLE 3 Suggestions for Increasing Platform-mediated Fairness ABOUT HERE

  • Because most concerns regarding workplace fairness, as expressed by the participants, address remuneration, compensation also emerges as a prominent theme in their discussion of possible avenues to increase fairness.
  • A more intermediated step toward fair compensation could be the stricter sanctioning of the deliberate or inadvertent misrepresentation of the time needed for tasks based on feedback mechanisms.
  • Currently, without the help of third-party software, workers receive very little information on requesters.
  • In many cases, interviewees advocate for some mechanism to systematically give input into platform design and management decisions, indicating a clear understanding of the crucial role platforms play in determining the fairness of digital work.
  • Workers who frame their platform engagement as a regular occupation struggle to find role concepts ensuring dignity and allowing for pride.

6. Conclusion

  • A number of observers have warned that the emergence of digital labor might lead to the development of a new digital precariat.
  • It should also be noted that, through a reduction of transaction costs, digital labor platforms facilitate the allocation of paid jobs previously too complex or finegrained to contract out.
  • These users frame the platform provider as more of an employer and accordingly apply more demanding criteria to the perceived fairness of the working environment.
  • These workers would presumably benefit by moving the governance mechanism of microworking platforms away from hierarchy and authority toward social capital and trust (Adler, 2001; Ouchi, 1980) .
  • Over the course of their research, the authors learned that the very specific background and life stories of workers deeply matter, both in terms of claims put forward against the platform, but also in the range of alternatives open to them.

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This file was downloaded from BI Open Archive, the institutional repository (open access)
at BI Norwegian Business School http://brage.bibsys.no/bi.
It contains the accepted and peer reviewed manuscript to the article cited below. It may
contain minor differences from the journal's pdf version.
Fieseler, C., Bucher, E., & Hoffmann, C. P. (2017, June 21). Unfairness by design? The
perceived fairness of digital labor on crowdworking platforms. Journal of Business Ethics.
Doi: https://doi.org/10.1007/s10551-017-3607-2
Copyright policy of Springer, the publisher of this journal:
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2
Unfairness by Design?
The Perceived Fairness of Digital Labor on Crowdworking Platforms
Abstract
Based on a qualitative survey among 203 US workers active on the microwork platform Amazon
Mechanical Turk, we analyze potential biases embedded in the institutional setting provided by on-
demand crowdworking platforms and their effect on perceived workplace fairness. We explore the
triadic relationship between employers, workers, and platform providers, focusing on the power of
platform providers to design settings and processes that affect workers’ fairness perceptions. Our
focus is on workers’ awareness of the new institutional setting, frames applied to the mediating
platform, and a differentiated analysis of distinct fairness dimensions.
Keywords: Crowdsourcing, Internet, Fairness, Digital Labor, Microwork, Crowdworking, Amazon
Mechanical Turk

3
Unfairness by Design?
The Perceived Fairness of Digital Labor on Crowdworking Platforms
1. Introduction
Digital platforms, such as Uber, Airbnb, TaskRabbit, and Amazon Mechanical Turk, have
brought disruptive change to many service industries. These platforms organize, facilitate, and bro-
ker the services provided by a dispersed workforce of hundreds of thousands of individuals
(“crowdwork”). The result is an emergence of digital piecework that differs from traditional low-
wage piecework in that it is no longer embedded in organizational hierarchies, but rather in a triadic
setting composed of clients (here: “requesters”), platform providers, and largely autonomous work-
ers. The workforce engaged on these digital on-demand service platforms is often characterized by
commodification, low cost, minimal institutionalization, and increasing anonymity.
In this article, we argue that digital on-demand crowdworking platforms constitute a new work
environment characterized by a triadic relationship between employers (requesters), workers, and
the platform provider. As designer of the platform, including its features, processes and af-
fordances, the provider plays a crucial role within this relationship. The provider is largely respon-
sible for determining working conditions. Yet, little is known about worker perceptions of these
responsibilities. For the purposes of this article, we follow the definition by Kittur et al. (2013, p.
1), who define crowdwork as “the performance of tasks online by distributed crowd workers who
are financially compensated by requesters (individuals, groups, or organizations).” This under-
standing of crowdwork implies a combination of organizational, individual, and technological as-
pects, thus conceptualizing crowdwork as asociotechnical work system(Kittur et al., 2013, p.

4
1). Our focus is the particular form of crowdwork most akin to piecework: microworking. Mi-
croworking is a form of freelance contracting on the Internet, for example carrying out human-
intelligence tasks on Amazon Mechanical Turk and Clickworker or by offering software develop-
ment or design skills via crowdsourcing platforms such as Upwork or 99designs.
The basic philosophy of microworking is to delegate tasks in the form of an open call addressing
an undefined but large group of people (Howe, 2009). The pieceworkers complete tasks in batches.
Employers can task these batches out through platforms such as Amazon Mechanical Turk. These
tasks might consist of the remote completion of small digital tasks, such as transcribing a snippet
of hand-written text, classifying an image, categorizing the sentiment expressed in a comment,
rating the relevancy of a search engine result, or selecting the most representative frame in a video
clip (Kittur et al., 2013; Lehdonvirta & Ernkvist, 2011). Digital workers are not paid by working
hours or hierarchical position. Rather, they are paid based on the timely completion of granular
work tasks.
Because crowdsourced digital piecework is a recent phenomenon, there is relatively little re-
search on the nature and effects of these emerging forms of work (e.g., Fish & Srinivasan, 2011;
Gehl, 2011; Kittur, Chi, & Suh, 2008; Silberman et al., 2010). Some researchers have examined
the desirability and fairness of piecework performed in crowdsourcing systems (Fish & Srinivasan,
2011; Kneese & Rosenblat, 2014). Others have focused on working conditions, such as reportedly
low wages (Ipeirotis, 2010; Ross, Irani, Silberman, Zaldivar, & Tomlinson, 2010). Most digital
service platforms function as spot markets, which are more temporary, part-time, remote, and mo-
bile than standard work arrangements (Connelly & Gallagher, 2004; Gregg, 2011; Rainie & Well-

5
man, 2012). Platform-mediated self-employed laborers remain largely detached from organiza-
tional structures (cf. Ashford, George, & Blatt, 2007). The quality of the relationship between plat-
form providers and workers remains contested. Platform providers exert significant influence over
the quality and quantity of tasks available to microworkers as well as overall working conditions
(Kingsley, Gray, & Suri, 2015; Rosenblat & Stark, 2015). Therefore, the perceived fairness of work
facilitated by digital microworking platforms can be expected to be shaped by the features of these
platforms.
In this article, we focus on the institutional environment constituted by these platforms, in par-
ticular microworking services. We analyze how platform characteristics affect the perceived fair-
ness, labor conditions, and outcomes based on a qualitative survey conducted among 203 US work-
ers active on the crowd-based service platform Amazon Mechanical Turk. Our analysis sheds light
on digital laborers’ evaluation of their working environment, their relationship with the platform
provider, and workers’ understanding of responsibilities for working conditions encountered on the
platform. Through this example, we show that digital on-demand service platforms constitute a
new institutional setting characterized by strong perceived power asymmetries. These asymmetries
are associated with variations in influence, autonomy, or “voice”, which ultimately affect the per-
ceived fairness of the labor facilitated by these platforms. We provide an in-depth analysis of work-
ers’ fairness perceptions by differentiating fairness dimensions and their respective antecedents.
Finally, we derive initial policy recommendations aimed at bolstering the conditions of digital la-
bor.

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Cites background from "Unfairness by Design? The Perceived..."

  • ...…platforms is unidirectional and not designed to facilitate communication between workers and the clients, which makes it difficult for workers to find out why their work was rejected, and this practice is unfair as they do not receive payments for the task performed (Feiseler et al., 2019)....

    [...]

  • ...Further, the flow of information on microtask platforms is unidirectional and not designed to facilitate communication between workers and the clients, which makes it difficult for workers to find out why their work was rejected, and this practice is unfair as they do not receive payments for the task performed (Feiseler et al., 2019)....

    [...]

  • ...This feature of the platform design needs to be corrected so as to ensure that there is communication between the different parties (Feiseler et al., 2019)....

    [...]

  • ...In addition, the platform design feature allows platforms to reject work deemed unsatisfactory without providing any explanation, and to withhold payment, which is a sign of power imbalance (Feiseler et al., 2019)....

    [...]

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Frequently Asked Questions (13)
Q1. What are the contributions in this paper?

Based on a qualitative survey among 203 US workers active on the microwork platform Amazon Mechanical Turk, the authors analyze potential biases embedded in the institutional setting provided by ondemand crowdworking platforms and their effect on perceived workplace fairness. The authors explore the triadic relationship between employers, workers, and platform providers, focusing on the power of platform providers to design settings and processes that affect workers ’ fairness perceptions. 

Unfortunately, their study did not get into much depth to interconnect these lines, but the authors would argue that it would be fruitful for future research to further explore these relationships and to connect to the biographies behind the roles performed on the platforms. Thus, it would be interesting in future research to not only look at the workers ’ perspective in isolation, but also consider the framing of the platforms, which might have led to the current discourse and levels of expectation. Future studies could focus on attempts by digital microworkers to affect the governance of platforms from within ( cf. Gray, Suri, & Kulkarni, 2016 ; Irani & Silberman, 2016 ; Soule, 2012 ). For that hope to become reality, further critical evaluations of the institutional setting and dynamics of these platforms are necessary and practicable measures to ameliorate asymmetries and bolster the fairness of digital labor remain to be explored. 

It should be noted that a governance mode reliant on rules and monitoring provides significant power to the agent setting these rules, who, in the case of digital labor, is the platform. 

the motivation and voluntariness of workers may play a key role in the analysis of digital labor fairness, similar to offline microwork settings. 

Procedural fairness may also play a particularly important role in the context of digital labor as platforms may systematically limit the scope and outcomes of work negotiations. 

The moral outrage expressed by these workers, such as using a comparison with servitude, may be aggravated by the fact that financial compensation is the only tangible measure of their work’s value. 

On an objective level, a number of respondents demand that platform-mediated work should be rewarded according to clear and transparent standards, such as national and regional minimal wages. 

There are some community-driven initiatives to ‘rehumanize’ the workforce that support ‘turkers’ (AMT workers) both informationally and emotionally, as well as adding enhancements to the AMT interface, such as TurkerNation, Turkopticion, MTurkGrind, Reddit’s /r/HITsWorthTurkingFor and Dynamo (Irani & Silberman, 2013; Salehi et al., 2015). 

By designing the platform, its features, processes, and affordances, the platform provider plays a key role in determining the antecedents and characteristics of (un)fairness in digital labor. 

The ability of employees to raise concerns and negotiate the terms of an exchange has been termed employee voice (Van Buren & Greenwood, 2008). 

The platform does, in fact, allow requesters to reject work deemed unsatisfactory and withhold payment with only minimal or no explanation provided. 

As is, sanctions are nearly exclusively applicable to workers, for example, by requesters rating unsatisfactory services or even withholding payment. 

These workers would presumably benefit by moving the governance mechanismof microworking platforms away from hierarchy and authority toward social capital and trust (Adler, 2001; Ouchi, 1980).