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

TLDR: 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.

Figures

TABLE 1 Relationships toward the Platform

TABLE 2 Fairness Perceptions

FIGURE 2 Classification of Platform Relationships

TABLE 3 Suggestions for increasing Platform-mediated Fairness

FIGURE 1 A typical microwork workflow and its challenges

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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
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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

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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 a “sociotechnical work system” (Kittur et al., 2013, p.

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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-

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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.

Frequently asked questions

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. 

Q2. What are the future works in this paper?

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. 

Q3. What is the role of the agent in the case of digital labor?

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. 

Q4. What is the role of employee voice in the analysis of digital labor fairness?

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

Q5. What is the role of procedural fairness in the context of digital labor?

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. 

Q6. What is the reason why workers are criticized for using a comparison with servitude?

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. 

Q7. What are the salient suggestions for enhancing platform-mediated fairness?

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. 

Q8. What are some community-driven initiatives to ‘rehumanize’ the workforce?

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). 

Q9. What is the role of the platform provider in determining the fairness perceptions of digital labor?

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. 

Q10. What is the definition of employee voice?

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

Q11. What does the platform allow requesters to do?

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

Q12. What is the common form of sanctions for workers?

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

Q13. What would be the benefits of moving the governance mechanism of microworking platforms away from hierarchy and authority?

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).