1/22/20, Week 1
Book: Ghost Work: How to Stop Silicon Valley from Building a New Global Underclass
Authors: Mary L. Gray, Siddharth Suri
Chapters: Introduction, Chapter 1
Summary:
In this book, Mary Gray and Sid Suri introduce the concept of “ghost work,” work that is done by people as part of a computational workflow, such as Uber’s face recognition system for drivers, or Facebook’s content moderators, among others. They highlight how a lot of technological, AI-backed advances involve large amounts of human labor that is often unseen, and thus, undervalued. This is a continuation of a centuries-long tradition of employing people on an as-needed basis. In this case, APIs hire humans for their creative and innovative inputs when AI systems encounter edge cases. This human input is then used as training data to improve the AI, and in turn, obviates the need for human input—at least temporarily. When developers attempt to push the boundaries further, they again encounter the limitations of AI and hire human annotators. This constant cycle of obviation and contingent recruitment is called the paradox of automation’s last mile.
Using a combination of ethnographic research and large-scale surveys, the authors highlight the context in which workers do this precarious work on four different platforms: MTurk, UHRS, LeadGenius, and Amara.org, and in two different countries: India and the US. They find that most workers value the flexibility—both temporal and spatial—afforded by this work, and yet, must deal with the precarity associated with this flexibility and a lack of legal protection: fluctuating income, no benefits, and the erasure of any semblance of a career ladder. They also find that platforms differ in their treatment of workers. By design, MTurk and UHRS isolate workers and provide no way for grievances to be redressed. On the other end of the spectrum are Amara and LeadGenius, where workers are valued and given opportunities to connect with each other.
Reflection:
This book shifts the focus on gig work from the requester to the worker; from efficient and cheap to flexible and cruel. The authors do so through a mixed-methods approach, which I appreciate. The ethnographic work highlights the context in which workers do this work and their motives for doing so. It also points to the inability of labor laws to keep up with technological advances, and how technology companies attempt to—and often succeed in—devaluing and abstracting away human labor in exchange for higher profits and stock prices. It would be interesting to see what the long-term psychological, physical, and economic effects are of gig work: on individuals, communities, and societies. I believe this will only increase the wealth inequality that we see throughout the world, and lead to wage stagnation and a lower quality of life for low-income workers. For example, recent work has pointed out the trauma that Facebook content moderators face on a daily basis [1, 2]. In addition, work has shown that workers’ decisions shape public discourse on social media platforms, which is in stark contrast to the “value-neutral” stance that social media platforms portray [3].
Of particular note is the authors’ problematization of automation and how the target is ever-moving, and that there will always be a need for human labor. The question then is how do we value this human labor in the technology supply chain? In addition, the erasure of the career ladder means that workers find it difficult to increase their wages. This points to the need for finding alternative ways to increase workers’ wages, and to train workers on-the-job. What happens when automation improves, and there is no longer a need for humans with a certain set of skills, but only for those who are highly skilled? This, too, points to the need for providing workers continuous on-the-job training.
Questions:
- How many of you have engaged in ghost work? [If you have ever completed a CAPTCHA/RECAPTCHA, you have.] How many of you would like to rely on ghost work for full-time employment? The average wage is about $2/hr on MTurk [4]. Do you think you would be able to survive on that?
- What are the long-term psychological, physical, and financial effects of doing ghost work?
- How do we value workers when algorithms intentionally abstract away their labor?
- How can workers be trained on-the-job? What are alternatives to a career ladder?
- What can you do to improve the condition of people who do ghost work?
References:
[1] Roberts, Sarah T. Behind the screen: Content moderation in the shadows of social media. Yale University Press, 2019.
[2] Newton, Casey. Bodies in Seats: At Facebook’s Worst-Performing Content Moderation Site in North America, one contractor has died, and others say they fear for their lives. The Verge. June 19, 2019. https://www.theverge.com/2019/6/19/18681845/facebook-moderator-interviews-video-trauma-ptsd-cognizant-tampa
[3] Gillespie, Tarleton. “Platforms are not intermediaries.” Georgetown Law Technology Review 2, no. 2 (2018): 198-216.
[4] Hara, Kotaro, Abigail Adams, Kristy Milland, Saiph Savage, Chris Callison-Burch, and Jeffrey P. Bigham. “A data-driven analysis of workers’ earnings on Amazon Mechanical Turk.” In Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems, pp. 1-14. 2018.
