Remote Work, Work Measurement and the State of Work Research in Human-Centred Computing

Over the past few decades, a small but growing group of people have worked remotely from their homes. With the arrival of the coronavirus pandemic, millions of people found themselves joining this group overnight. In this position paper, we examine the kinds of work that ‘went remote’ in response to the pandemic, and consider the ways in which this transition was influenced by (and in turn came to influence) contemporary trends in digital workplace measurement and evaluation. We see that employers appeared reluctant to let certain classes of employee work remotely. When the pandemic forced staff home, employers compensated by turning to digital surveillance tools, even though, as we argue, these tools seem unable to overcome the significant conceptual barriers to understanding how people are working. We also observed that, in the United Kingdom context, the pandemic didn’t mean remote work for a significant proportion of the population. We assert that, to maximize its impact, ‘future of work’ research in human-centred computing must be more inclusive and representative of work, rather than focusing on the experiences of knowledge workers and those involved in new forms of work

The future of techno-disruption in gig economy workforces: challenging the dialogue with fictional abstracts

In this article we explore near-future of the pervasive computing, AI, and HCI in the context of the disruptive potential of technologies on workers in the on-demand gig economy. Using fictional abstracts, the authors muse on dystopian case studies of: independent contractors, last-mile couriers, teachers, and creative professionals. This article serves as base for critical reflections on: 1) the need for multidisciplinary approaches when tackling broader and far-reaching societal implications of digital technology in the gig economy, and 2) the potential role of fictional abstracts in the design process of future digital technologies

CrowdCO-OP : sharing risks and rewards in crowdsourcing

Paid micro-task crowdsourcing has gained in popularity partly due to the increasing need for large-scale manually labelled datasets which are often used to train and evaluate Artificial Intelligence systems. Modern paid crowdsourcing platforms use a piecework approach to rewards, meaning that workers are paid for each task they complete, given that their work quality is considered sufficient by the requester or the platform. Such an approach creates risks for workers; their work may be rejected without being rewarded, and they may be working on poorly rewarded tasks, in light of the disproportionate time required to complete them. As a result, recent research has shown that crowd workers may tend to choose specific, simple, and familiar tasks and avoid new requesters to manage these risks. In this paper, we propose a novel crowdsourcing reward mechanism that allows workers to share these risks and achieve a standardized hourly wage equal for all participating workers. Reward-focused workers can thereby take up challenging and complex HITs without bearing the financial risk of not being rewarded for completed work. We experimentally compare different crowd reward schemes and observe their impact on worker performance and satisfaction. Our results show that 1) workers clearly perceive the benefits of the proposed reward scheme, 2) work effectiveness and efficiency are not impacted as compared to those of the piecework scheme, and 3) the presence of slow workers is limited and does not disrupt the proposed cooperation-based approaches

Remote work, work measurement, and the state of work research in human-centred computing

Over the last few decades, a small but growing group of people have worked remotely from their homes. With the arrival of the coronavirus pandemic, millions of people found themselves joining this group overnight. In this position paper, we examine the kinds of work that 'went remote' in response to the pandemic, and consider the ways in which this transition was influenced by (and in turn came to influence) contemporary trends in digital workplace measurement and evaluation. We see that employers appeared reluctant to let certain classes of employee work remotely. When the pandemic forced staff home, employers compensated by turning to digital surveillance tools, even though, as we argue, these tools seem unable to overcome the significant conceptual barriers to understanding how people are working. We also observed that, in the United Kingdom context, the pandemic didn't mean remote work for a significant proportion of the population. We assert that, to maximise its impact, 'future of work' research in Human-Centred Computing must be more inclusive and representative of work, rather than focusing on the experiences of knowledge workers and those involved in new forms of work

An Analysis of Upwork Profiles: Visualizing Characteristics of Gig Workers using Digital Platform

In the transitional period of manual and algorithmic hiring, there has been an explosion of new employment opportunities. As a result of the ubiquity of mobile communication technologies, a gig economy has emerged which champions digital platforms as a solution to meet the burgeoning demand for on-demand workers, frequently called independent contractors or freelancers, by hiring organizations. Visualizations and linear regression are used to study information-rich Upwork profiles to determine variables that could predict how users maneuver in the gig economy. A typology of existing gig workers’ motivations is combined with visualizations to better understand the situation of the typical gig economy worker.Master of Science in Information Scienc

Unleashing the Potential of Crowd Work: The Need for a Post-Taylorism Crowdsourcing Model

Paid crowdsourcing connects task requesters to a globalized, skilled workforce that is available 24/7. In doing so, this new labor model promises not only to complete work faster and more efficiently than any previous approach but also to harness the best of our collective capacities. Nevertheless, for almost a decade now, crowdsourcing has been limited to addressing rather straightforward and simple tasks. Large-scale innovation, creativity, and wicked problem solving are still largely out of the crowd’s reach. In this opinion paper, we argue that existing crowdsourcing practices bear significant resemblance to the management paradigm of Taylorism. Although criticized and often abandoned by modern organizations, Taylorism principles are prevalent in many crowdsourcing platforms, which employ practices such as the forceful decomposition of all tasks regardless of their knowledge nature and the disallowing of worker interactions, which diminish worker motivation and performance. We argue that a shift toward post-Taylorism is necessary to enable the crowd address at scale the complex problems that form the backbone of today’s knowledge economy. Drawing from recent literature, we highlight four design rules that can help make this shift, namely, endorsing social crowd networks, encouraging teamwork, scaffolding ownership of one’s work within the crowd, and leveraging algorithm-guided worker self-coordination.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/171075/1/Lykourentzou et al. 2021.pdfDescription of Lykourentzou et al. 2021.pdf : Final ArticleSEL

I diritti dei lavoratori digitali nella prospettiva del Pilastro sociale. WP CSDLE “Massimo D’Antona”.INT – 146/2017 = The rights of digital workers in the perspective of the Social Pillar. WP CSDLE “Massimo D’Antona”.INT – 146/2017

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