Ghost work

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Ghost Work was a term coined by anthropologist Mary L. Gray and computer scientist Siddharth Suri in their 2019 book, Ghost Work: How to Stop Silicon Valley from Building a New Global Underclass.[1] The authors' intended use of the term "Ghost Work" is to call out work conditions where the value of the person providing momentary service is erased.[2]

Definition[edit]

“Ghost Work" does not describe the work itself, but the conditions of work. It focuses on work that is task-based and content-driven that can be funneled through the internet and APIs, Application programming interfaces. This kind of work can include labeling, editing, moderating, and sorting information or content. For example, whenever YouTube's algorithm or AI recommends a video to a user, it's due to the work that someone did to program it.[1]"

An example of this type of work and where it began, comes from Amazon (company). As the retailer grew, they realized that they would need to constantly post products, verify product photos, create product captions, and more. In addition to these tasks, Amazon also needed an army of people to fix up book reviews back in 2005, so they created a website, MTurk, where tasks could be posted for others to complete. Once these tasks were completed, the person who completed it would be paid. Amazon also charged a small surcharge to match posters with those who had certain qualifications to complete the projects and tasks. This allowed almost anyone to go on and find work.[3]

"Ghost Work" is also work that can be done remotely (wherever they have internet access) and on a contract-basis.[3] It's an invisible workforce made up of those who treat it as a full-time job and those who pick it up whenever they have the time.[4] Though it can work position-independent through the internet, there are data factories in China that mine “the Saudi Arabia of data” by parsing and cataloguing to make data useful and then assemble the foundation of the nation’s AI ambition.[5] The core characteristics of low-wage, disposable, boring and no growth of the ghost work retained. The ghost workers are the low-tech part of the high-tech production, as the construction workers in the digital world. The fear of one day AI will take their jobs is more obvious.

One of the benefits of "Ghost Work" is that it can allow for flexible hours due to the worker choosing when they complete a task. This can make it appealing for many who may not be able to find work elsewhere due to many different circumstances.[4]

With the promise of flexible hours and endless tasks, companies can potentially undervalue, under appreciate or under compensate workers. However the workforce today is beginning to turn more towards this way of work, similar to Uber and Lyft drivers, rather than the standard 9-5 style of work .[1]

In contrast to peer production that emphasizes the community spirit and co-work on open source products, the ghost works tend to be benefit-driven.

"Ghost Work" is different from the gig work or temporary work because temporary and gig work are considered more full time and project based, rather than task based. While the gig work includes more general platform work, the ghost work emphasizes on software or algorithm aspect of assisting the machine to automate further. Through labelling the content, the ghost workers teach the machine to learn as defined by Gray and Suri “human labor powering many mobile phone apps, websites, and artificial intelligence systems".[6]

The devaluation of momentary service[edit]

The concept of hiring people on the assumption that they will be around only for the duration of a finite project or that the presumed efficiencies of an automated process can replace those workers is not radically new. By the late 1800s, Lowell mills paid farm families to hand-fashion cloth pieces into shirt flourishes that were still too delicate to churn out on the factory floor.[7] Equivalently, today's companies hire on-demand workers to test the latest ranking, relevance, and crawling algorithms of their search engines so they can be perfected.

Contingent work was further devalued by culturally loaded notions about what counted as a learned profession or "skillful" work, and which workers deserved or needed full-time jobs. Farm wives sewing, a continent of "others" doing data entry offshore, a contract worker helping with a speculative education software package that may or may not ship. could be written off, in part, because of their gender, skin color, nationality, professional training, physical location, or all of the above.

Those doing on-demand jobs today are the latest iteration of the expendable ghost work. They are, on the one hand, necessary in the moment, but they are too easily devalued because the tasks that they do are typically dismissed as mundane or rote and the people often employed to do them carry no cultural clout.[3]

External image[edit]

Computer science world and a variety of tech companies, in particular, are invested in producing the image of technological magic. For instance, Amazon Mechanical Turk; a place to hide the people involved in the production of so called magic, whose visibility would otherwise obstruct favorable perception. The following isn't only aimed at public image of the company but also at investors, who are significantly more likely to back businesses built on scalable technology, not unwieldy workforces demanding office space and minimum wages. In addition, there exists a pervasive belief among engineers that these workers are a stop-gap until AI can replace or do without them, which inevitably leads to their vital contribution being devalued. Despite the belief, the market for ghost work doesn't show apparent signs of declining. If anything, it's contrary.[1]

References[edit]

  1. ^ a b c d Gent, Edd. "The 'ghost work' powering tech magic". www.bbc.com. Retrieved 2019-10-22.
  2. ^ "A walk with Mary Gray - Long version interview - YouTube". www.youtube.com. Retrieved 2020-12-25.
  3. ^ a b c Gray, Mary L., author. Ghost work : how to stop Silicon Valley from building a new global underclass. ISBN 9781328566287. OCLC 1052904039.CS1 maint: multiple names: authors list (link)
  4. ^ a b "Ghost Work and the Future of Employment - MIT Technology Review". MIT Technology Review Events. Retrieved 2019-10-22.
  5. ^ Yuan, Li (2018-11-25). "How Cheap Labor Drives China's A.I. Ambitions (Published 2018)". The New York Times. ISSN 0362-4331. Retrieved 2020-11-30.
  6. ^ Shestakofsky, Benjamin (October 2019). "Book Review: Ghost Work: How to Stop Silicon Valley from Building a New Global Underclass . By Mary L. Gray and Siddharth Suri". ILR Review. 72 (5): 1283–1285. doi:10.1177/0019793919864564. ISSN 0019-7939.
  7. ^ "Early Industrialization in the Northeast | United States History I". courses.lumenlearning.com. Retrieved 2020-12-26.