MY BOSS IS AN AI: AI AND THE TRANSFORMATION OF HOME-BASED WORK

Call for Contributions to a Special Section in the International Journal of Communication

Edited by Luke Munn, Digital Cultures and Societies, University of Queensland

As work shifts to the home, AI follows. Working from home has surged in the wake of pandemic lockdowns, leading to millions more workers operating remotely outside of traditional work contexts (Barrero et al. 2021). It is estimated that 557 million individuals worked from home during the second quarter of 2020 (Soares et al. 2021). And while restrictions have largely been lifted in this so-called post-COVID era, some workers have refused to return to the office, creating a situation where WFH is the new normal (Williamson et al. 2020; Abdullah et al. 2020). #wfh 

AI companies have pivoted quickly to take advantage of this mass migration. AI technologies are now used in a variety of home office tasks, from monitoring productivity to scheduling work and generating content. Recent research has begun to grapple with this shift, examining work/life balance (Bellmann and Hübler 2020), the challenge of multitasking, (Xu et al. 2021) and the extent of distraction (Leroy et al. 2021)—yet the infiltration and influence of AI in home-work has yet to be fully appreciated and understood. The need for this analysis becomes even more urgent as AI’s ability to exacerbate race, gender, and class inequality becomes clear (Precarity Lab 2020). #futureofwork

Of particular interest here is the rise in “bossware,” which remotely surveills workers in increasingly invasive and articulated ways (Corbyn 2022). One survey of 1250 businesses in the US found that 60% of companies were using monitoring software to track employee activity and productivity (Digital.com 2022). This software can log which apps are used, which sites are visited, and even which keyboard keys are tapped (Bloomberg 2020). Some of these services flag employees that are deemed risky. Many, like Microsoft 365, claim to show how industrious workers are, and offer these “productivity scores” to management as a tool for optimizing their business (Carter 2021). #bossware

Certainly the drive to track and optimize work has a long history, from Taylorism to Toyotism, and a genealogy of these moments could help to historicize this phenomenon and reveal its commonalities with previous regimes in the context of capitalism. However, we also see an array of novel elements at play here, in terms of the granular kind of feedback on offer, the mobility of this software (moving with workers from their home desktops to their mobile phones at the cafe), and the ability of AI / ML models to rapidly assemble this vast amount of data into powerful “scores” and “insights.” What then is old, what is merely intensified, and what is truly new? #histories

These shifts have not been accepted without resistance. One survey suggested that 6 out of 10 workers would not choose a job where their employer used software to track their work (Graham 2021). Hardware-based mouse movers and software-based mouse jigglers have emerged as a counter-tactic to tracking, allowing workers to emulate activity even when away from their desk. These devices have proliferated, with some seeing double digit growth since the start of the COVID era (Lewis 2021). We can anticipate that such counter-measures would be joined by others, such as boosting metrics and gamifying performance frameworks. #resistance

These systems shape the subjectivity of workers. Employees expressed a “pronounced sense of unfairness and lack of agency” around the automated decisions being made without their awareness or approval (Skelton 2021). Other reports suggest that algorithmic tracking is damaging the mental health of workers (Milmo 2021). However, perhaps the most telling trend is precisely a lack of affective response or active resistance. As this approach becomes more pervasive and ubiquitous, workers report initially feeling violated, but then getting used to it (Drapkin 2022). #affect

The impacts of these transformations are racialized and gendered. In a recent survey, only 3% of Black workers wanted to return to full-time in-person work vs 21% of white workers (Combs 2021). This suggests that AI-driven transformations in remote work will disproportionately affect such groups, who may prefer home-based work as it allows them to juggle child care, community work, care work, and other commitments. In terms of gender, men are more likely to be offered remote work, even though women desire it more; remote-work opportunities are even slimmer for transgender and non-binary people (Thier 2022). Remote-work, then, is a life-chance offered to some and not to others, resonating with the broader history of labor inequality (Munn 2022). #inequality

While such tools are undoubtedly invasive and problematic (Cypher and Gullo 2020), they are already in operation. Rather than polemical takedowns or simple dismissals, then, we encourage submissions that investigate the particular ways that these systems shape labor and the lived reality of workers. How do AI tools change the outputs of workers, the structure of their workday, and their relationship with coworkers and management? Alongside monitoring, then, contributions might look at how AI transforms work contexts (Holmström and Hällgren 2021) or attempts to implement data-driven project management (Auth et al. 2019). We welcome these kinds of grounded, specific investigations into the transformations and trade-offs that AI introduces into remote work. #casestudies 

Building on these insights, potential research questions include:

• How might we map the use of AI in work-at-home? What are the apps, services, and platforms in use? And how do these differ across countries and contexts? #wfh

• What is the logic of AI software for home-based work? What does it promise workers? What does it offer to management? Are these claims fulfilled? #futureofwork

• How is remote management software (bossware) used in practice? What features are used, how accurate are metrics, and how aware are workers of these tools? #casestudies

• How do workers push back against these tools, whether through software and hardware, activism and organization, or less spectacular moves (quiet quitting, feet-dragging)? #resistance

• How does remote surveillance line up with broader histories of surveillance by management? How does productivity software slot into longer genealogies of worker optimization and control? #histories

• How does this digital regime shape the subjectivity of workers, their anxieties, stress levels, and general feeling toward their coworkers and employers? #affect

• How does AI exacerbate already existing inequalities in terms of race, gender, disability, religion, and so on? How exactly do these power asymmetries manifest? #inequality

This Special Section will contribute to our understanding of AI in the context of home-based work: how it is used, who it benefits, and particularly how it shapes the politics, power, and subjectivity of labor in particular ways. In this sense, the Section will provide a much-needed portrait of contemporary work “on the ground” in our post-COVID context. AI platforms and services are powerful and influential, but have often been rolled out silently, with little opportunity for reflection or discussion. This Special Issue thus also seeks to offer a space for dialogue, laying out the implications of these profound transformations and opening up our assumed “future of work” to debate and contestation. 

Submissions

This Special Section in the International Journal of Communication will aim to include 4-6 research articles on these themes. Research articles should be between 6000 and 8900 words, inclusive of references. Rich media (images, video, audio) may also be included provided the author has all the necessary copyright permissions. Articles will be double-blind reviewed. To be considered, please send an abstract (150-300 words) and short bio to Dr Luke Munn (l.munn@uq.edu.au) by November 10. All submissions will be reviewed within two weeks. 

References

Abdullah, Nur Afiqah Akmal, Noor Hanim Rahmat, Fatin Zafirah Zawawi, Muhammad Adib Nazhan Khamsah, and Afiqah Humaira Anuarsham. 2020. “Coping with Post COVID-19: Can Work from Home Be a New Norm?” European Journal of Social Sciences Studies 5 (6).

Auth, Gunnar, Oliver Jokisch, and Christian Dürk. 2019. “Revisiting Automated Project Management in the Digital Age – a Survey of AI Approaches.” Online Journal of Applied Knowledge Management 7 (1): 27–39.https://doi.org/10.36965/OJAKM.2019.7(1)27-39.

Bellmann, Lutz, and Olaf Hübler. 2020. “Working from Home, Job Satisfaction and Work–Life Balance – Robust or Heterogeneous Links?” International Journal of Manpower 42 (3): 424–41.https://doi.org/10.1108/IJM-10-2019-0458.

Bloomberg, dir. 2020. How Bossware Is Watching While You Work.https://www.youtube.com/watch?v=rLjZ6mbodcE.

Carter, Rebekah. 2021. “Understanding Microsoft Productivity Score.” UC Today. January 15, 2021.https://www.uctoday.com/collaboration/understanding-microsoft-productivity-score/.

Combs, Veronica. 2021. “Slack Survey Finds 97% of Black Knowledge Workers Want the Future of the Office to Be Remote or Hybrid.” TechRepublic. March 11, 2021.https://www.techrepublic.com/article/slack-survey-finds-97-of-black-knowledge-workers-want-the-future-of-the-office-to-be-remote-or-hybrid/.

Corbyn, Zoë. 2022. “‘Bossware Is Coming for Almost Every Worker’: The Software You Might Not Realize Is Watching You.” The Guardian, April 27, 2022, sec. Technology.https://www.theguardian.com/technology/2022/apr/27/remote-work-software-home-surveillance-computer-monitoring-pandemic.

Digital.com. 2022. “6 in 10 Employers Require Monitoring Software for Remote Workers.” Digital.Com. January 31, 2022.https://digital.com/6-in-10-employers-require-monitoring-software-for-remote-workers/.

Drapkin, Aaron. 2022. “‘I Felt Violated… Then Got Used to It’ - Employee Monitoring Divides Opinion.” Tech.Co (blog). February 24, 2022.https://tech.co/news/employee-monitoring-software-divides-opinion.

Graham, August. 2021. “Most Remote Workers Reject Monitoring Software, Study Finds.” Evening Standard, March 28, 2021, sec. News.https://www.standard.co.uk/news/uk/yougov-graham-workers-men-england-b926739.html.

Holmström, Jonny, and Markus Hällgren. 2021. “AI Management beyond the Hype: Exploring the Co-Constitution of AI and Organizational Context.” AI & SOCIETY, July.https://doi.org/10.1007/s00146-021-01249-2.

Leroy, Sophie, Aaron M Schmidt, and Nora Madjar. 2021. “Working from Home during COVID-19: A Study of the Interruption Landscape.” Journal of Applied Psychology 106 (10): 1448.

Lewis, Norman. 2021. “Remote Workers Find Ways to Trick ‘Bossware’ Spying.” RT International. November 12, 2021.https://www.rt.com/op-ed/542905-bossware-spying-mouse-mover/.

Milmo, Dan. 2021. “Algorithmic Tracking Is ‘Damaging Mental Health’ of UK Workers.” The Guardian, November 11, 2021, sec. Technology.https://www.theguardian.com/technology/2021/nov/11/algorithmic-monitoring-mental-health-uk-employees.

Munn, Luke. 2022. Automation Is a Myth. Stanford: Stanford University Press.

Precarity Lab. 2020. Technoprecarious. Cambridge, MA: MIT Press.

Skelton, Sebastian. 2021. “New Law Needed to Rein in AI-Powered Workplace Surveillance.” ComputerWeekly.Com. December 11, 2021.https://www.computerweekly.com/news/252509431/New-law-needed-to-rein-in-AI-powered-workplace-surveillance.

Thier, Jane. 2022. “The Gender Gap Has Come for Remote Work.” Fortune, June 29, 2022.https://fortune.com/2022/06/28/remote-work-gender-gap-men-versus-women-opportunities/.

Williamson, Sue, Linda Colley, and Sally Hanna-Osborne. 2020. “Will Working from Home Become the ‘New Normal’ in the Public Sector?” Australian Journal of Public Administration 79 (4): 601–7.https://doi.org/10.1111/1467-8500.12444.

Xu, Shan, Kerk Kee, and Chang Mao. 2021. “Multitasking and Work-Life Balance: Explicating Multitasking When Working from Home.” Journal of Broadcasting & Electronic Media 65 (3): 397–425.https://doi.org/10.1080/08838151.2021.1976779.