The essay proposes to use alienation as a heuristic and conceptual tool for the analysis of the impact of digital information and communication technologies (ICTs) on users. It follows a historical materialist understanding, according to which data can be considered as things produced in an industrial fashion. A representational interpretation, according to which data would merely reflect a given reality, is untenable. It will be argued instead to understand data as an additional layer which has a transformative impact on reality (...) as a whole. The analysis follows the early Marx in thinking of alienation as a structural condition which holds even if it is not experienced psychologically by users. Alienation means that the desire to express oneself and achieve social recognition, self-realization, and participation through the use of ICTs is incongruent to the inherent purposes and functions of these technologies. Digital platforms create the ideological illusion that users contribute to the production of data solely for their own gratification. Users, in turn, react to structural alienation either by internalizing it, in which case they may become enthusiastic data producers, or by imposing alienating conditions on other users. Finally, it will be shown that a strong notion of privacy is less suited to articulate concerns about the impact of ICTs because it expresses a desire for privilege and individual control which has become unfulfillable in the environment of data industries. The essay closes by showing some of the changes in institutional structures and behavior that could follow from the focus on alienation. (shrink)

Gender discrimination continues to plague organizations. While the advent of the Internet and the digitization of commerce have provided both a mechanism by which goods and services can be exchanged, as well as an efficient way for consumers to voice their opinions about retailers (i.e., via online rating systems), recent work has begun to uncover significant biases that manifest during the review process. In particular, it has been suggested that the gig-economy’s elimination of previously anonymous arm’s-length transactions may re-introduce bias (...) into perceptions of quality. In this work, we build upon research that has identified biases based on ascriptive characteristics in rating systems of ridesharing platforms. In doing so, we conduct an online experiment using a series of factorial vignettes to consider not only willingness to transact but post-transaction perceptions of quality. Findings suggest that female drivers are not penalized ex ante for taking on gender incongruent roles, i.e., driving, and experience no penalty when a rider reports a high-quality experience. However, conditional upon a lower quality experience, there is a disproportionate penalty for female drivers. Strikingly, we also observe that historic high quality does not serve as a buffer against such penalties, as prior literature would suggest. These findings underscore the challenges platforms face in ensuring their participants are being evaluated in a fair and impartial way, and highlight the difficulties present when non-employees (customers) are rating non-employees (drivers) of the firm. (shrink)

This article examines whether a crowdsourced research participant who quits a study before its completion should receive any monetary compensation. The study is focused on participants recruited from Amazon Mechanical Turk, the most widely used crowdsourcing platform, and examines the tensions between participants’ rights and research objectives when online labor markets are used to recruit research participants. The discussion is informed by the recent literature on online research with crowdsourced samples, evidence from human subjects’ practices at top US universities, and (...) an ethical analysis based on distributive justice and consequentialism. The results indicate that compensating crowdsourced research subjects who fail to complete their participation jeopardizes the benefits of many who dutifully participate and undermines the overall value of the research enterprise for advancing knowledge. By examining the issue of payment for crowdsourced participants who drop out, this article sheds light on modern ethical issues in online research and offers practical recommendations for researchers and ethics scholars. (shrink)

Purpose Underlying much recent development in data science and artificial intelligence (AI) is a dependence on the labour of precarious crowdworkers via platforms such as Amazon Mechanical Turk. These platforms have been widely critiqued for their exploitative labour relations, and over recent years, there have been various efforts by academic researchers to develop interventions aimed at improving labour conditions. The aim of this paper is to explore US-based crowdworkers’ views on two proposed interventions: a browser plugin that detects automated quality (...) control “Gold Question” (GQ) checks and a proposal for a crowdworker co-operative. Design/methodology/approach The authors interviewed 20 US-based crowdworkers and undertook a thematic analysis of collected data. Findings The findings indicate that US-based crowdworkers tend to have negative and mixed feelings about the GQ detector, but were more enthusiastic about the crowdworker co-operative. Originality/value Drawing on theories of precarious labour, this study suggests an explanation for the findings based on US-based workers’ objective and subjective experiences of precarity. The authors argue that for US-based crowdworkers “constructive” interventions such as a crowdworker co-operative have more potential to improve labour conditions. (shrink)

The COVID-19 pandemic has intensified the extent to which economies in the developed and developing world rely on gig workers to perform essential tasks such as health care, personal transport, food and package delivery, and ad hoc tasking services. As a result, workers who provide such services are no longer perceived as mere low-skilled laborers, but as essential workers who fulfill a crucial role in society. The newly elevated moral and economic status of these workers increases consumer demand for corporate (...) social responsibility regarding this stakeholder group – specifically for practices that increase worker freedom and rewards. We provide algorithmic tools for online labor platforms to meet this demand, thereby bolstering their social purpose and ethical branding while better protecting themselves from future reputational crises. To do so, we advance a managerial strategy rooted in moral self-awareness theory in order to leverage customers’ virtuous self-perception and increase gig-worker freedom. (shrink)