Bias in Online Freelance Marketplaces: Evidence from TaskRabbit and Fiverr

By Anikó Hannák, Claudia Wagner, David García, Alan Mislove, M. Strohmaier, Christo Wilson. Conference on Computer Supported Cooperative Work, 2017

325 citations recorded by the source corpus.

Online freelancing marketplaces have grown quickly in recent years. In theory, these sites offer workers the ability to earn money without the obligations and potential social biases associated with traditional employment frameworks. In this paper, we study whether two prominent online freelance marketplaces - TaskRabbit and Fiverr - are impacted by racial and gender bias. From these two platforms, we collect 13,500 worker profiles and gather information about workers' gender, race, customer reviews, ratings, and positions in search rankings. In both marketplaces, we find evidence of bias: we find that gender and race are significantly correlated with worker evaluations, which could harm the employment opportunities afforded to the workers. We hope that our study fuels more research on the presence and implications of discrimination in online environments.

Subfields: Marketing, Sociology and Political Science

View Paper: https://www.semanticscholar.org/paper/55049ac72bcea234cec51f087945a9347fb61bc9

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Citations in the corpus

Do News Consumers Want Explanations for Personalized News Rankings (2017, methodology): “The framework for optimizing task assignment in the field of knowledge intensive tasks introduced by [13] prevents an over or under utilization of the workers but the influence on the involved actors, e.”
Academic performance prediction in a gender-imbalanced environment (2017, background): “Algorithmic biases have been demonstrated in the systems that make decisions (or aid the human decision making process) in areas as diverse as loans [10], parole [12], hiring [10], and policing [15].”
Machine behaviour (2019, background): “Online pricing algorithms shape the cost of products differentially across consumers 23–25 .”
Discrimination through Optimization (2019, background): “s “black boxes” and observe their reactions to di˛erent inputs. Among most notable results, researchers have shown price discrimination in online retail sites [40], gender discrimination in job sites [15, 41], stereotypical gender roles re-enforced by online translation services [12] and image searc”
What Do Platforms Do? Understanding the Gig Economy (2020, background): “A study of TaskRabbit and Fiverr (Hannák et al. 2017) found that women and, particularly, workers of color receive fewer reviews and lower ratings.”
Potential for Discrimination in Online Targeted Advertising (2018, background): “Much recent work has focused on detecting instances of discrimination in online services ranging from discriminatory pricing on e-commerce and travel sites like Staples (Mikians et al., 2012) and Hotels.com (Hannák et al., 2014) to discriminatory prioritization of service requests and oﬀer-ings from”
Smart, Responsible, and Upper Caste Only: Measuring Caste Attitudes through Large-Scale Analysis of Matrimonial Profiles (2019, unknown): “This harm-benefit reasoning is analogous to those used by audit studies studying discrimination on platforms whose TOS prohibit such activities (Hannák et al. 2017; Chen et al. 2018).”
Actionable Auditing: Investigating the Impact of Publicly Naming Biased Performance Results of Commercial AI Products (2019, background): “Targeted public algorithmic audits provide one mechanism to incentivize corporations to address the algorithmic bias present in data-centric technologies that continue to play an integral role in daily life, from governing access to information and economic opportunities to influencing personal free”