PhD Student in Information Science at University of Colorado Boulder
Ghost WorkGray, M. L., Suri, S. (2019). Ghost Work: How to Stop Silicon Valley from Building a New Global Underclass. United States: HMH Books.
Ghost work: the opaque world of human labor undergirding AI, websites, apps, etc.
Micro-tasks: Small, repetitive tasks
Macro-tasks: Tasks that require thoughtfulness and searching
Labor arbitage: Seeking the lowest cost workforce with comparable quality, generally in countries with lower pay standards
Localization: Seeking labor in locales specific to the product
The introduction chapter focuses on introducing the reader to the concept of ghost work, as well as vignettes of those doing ghost work, like gig workers on Amazon Mechnical Turk flagging inappropriate material for social media websites for $40 every 10 hours. Crowdsourcing, microwork, crowdwork, and human computation refer to the types of tasks that involve routing decisions to humans to complete. The authors describe the links between platforms and crowdworkers, using Uber as an example. When an Uber driver is flagged, like when facial recognition fails, Uber routes the task of confirming the driver through crowdsource platforms, like CrowdFlower, for a crowdworker to complete: "a growing supply chain of services that use APIs and human computation" (pg. xvi). These jobs are described as part of a "Second Machine Age" or "Fourth Industrial Revolution." They often have no legal status or hourly wage. They refer to the view of exploiting human labor through APIs as "algorithmic cruelty."
They discuss how automation creates new jobs for humans, calling the strive for full automation "the paradoxof automation's last mile" (pg. xxii). "The last mile" in this case describes "the gap between what a person can do and what a computer can do." They discuss how ghost work is difficult to track. The US Bureau of Labor statistics asked about "contingent jobs" but only in the context of longterm primary jobs (10.1%), so those working gig work as a side job were not counted, nevermind the fuzziness of terms like "primary" and "longterm."
Those working in ghost work jobs are often seeking something current labor conditions cannot give them, something beyond minimum wage in retail, including flexibility, time and proximity to family, and new skills. The authors conducted over 200 interviews over 5 years, and collected over 10,000 survey responses, focused on India and the US. Their participants worked using four different platforms: AMT, UHRS, LeadGenius, and Amara.org. They outline the benefits and the harms of ghost work.
Humans in the LoopIn this chapter, they trace the origins of ghost work to Amazon's beginnings, which required an army of contract workers to help clean up book catalogues. In 2005, they debuted AMT. Originally for cleaning up product listings and typos in reviews, they opened AMT to requesters to pay for small tasks and charged a surcharge for matching requested with workers. Its debut was aligned with a building global recession that saw over 100k workers sign up by 2007. They discuss how APIs have replaced employers by automating scheduling and task assignment, making opaque who the requester is and who the worker is. They discuss how Mechanical Turk, a parlor game that acted as a Wizard of Oz type trick where chess masters pretending to be automated, serves to dehumanize workers by replacing them with IDs; people forget humans are the ones doing the work on the other side.
They refer to ImageNet as an early example of mass scale use of AMT, making the 19 year task of labeling 3.7 million images into 2.5 year task done by 49k workers from 167 countries. They cite ImageNet and its human-labeled "gold standard" ground truth as fueling the AI revolution. Since AMT, more companies have popped up, even offering macro-task work (rather than micro-task, like labeling one image). These companies skirt around legal classifications of employment.
The Workers of AMTThey begin describing the types of workers they interviewed and observed. The majority of Turkers (70%) have a bachelor degree and skew young (~77% between 18-37) (pg. 10). 75% of workers have another source of income beyond AMT. No labor agencies track workers on AMT, but Amazon claims to have over 500k workers, with more than 2500 active at a time. The minimum fee per task is one cent, with an average equivalent of $11 per hr. However, most tasks are low paying. The work may also be rejected by the requester if they deem it low quality, and most tasks require an approval rating of 95% or higher. After two years, one of their participants, makes minimum wage working on AMT ($7.25 hr, almost 16k a year). Only 4% of workers are considered skilled enough to make that much. Workers are also responsible for their own income tax, expected to file as independent contractors.
AMT also restricts how income can be used. In the US, workers can choose an Amazon gift card, or choose to put it into an Amazon Pay account. To transfer from Amazon Pay to their own bank accounts, they must pay a transfer fee to Amazon. Most international workers (besides India) can only choose a gift card. In the US, workers must first submit 10 days of requester-approved work; in India, Amazon must first verify a worker's PAN information which takes a week or more.
Vendor Management Systems (VMS)UHRS is internal to Microsoft, and uses a VMS to recruit ghost workers under NDAs to do tasks on behalf of the company, doing tasks like beta testing, checking code, and improving proprietary algorithms. UHRS demographics: ~80% ages 18-37, 70%+ male, 85%~ bachelor's degree or more. Yet there are still no clear labor laws with VMSs.
Companies using VMSs or AMT do not necessarily reveal themselves and may use codenames rather than real company names.
LeadGeniusA business-to-business service that sells leads to salespeople using human labor to determine whether to information is useful to the business looking for sales leads. The authors describe this business as providing somewhere between micro- and macro-tasks. Unlike the other ghost work platforms, LeadGenius employs interviews conducted by other workers, and the process can take 3 weeks. Workers who pass the hiring process start on a 90 day trial, then they can get an 8% bump in pay. Workers must commit to 20 hours a week. Workers are 85% 18-37, 70%+ bachelor's holders, and 49% women. 75% of workers do some other form of work. LeadGenius has a more traditional work model, where workers can accelerate to different roles and work in teams. Unlike AMT or UHRS, workers can turn to managers for help on their dashboards.
AmaraAmara is a translation and video captioning service that uses both automation and ghost work, which also blends volunteer work and paid work. Their participant, Karen, started off working as a volunteer and then moved to Amara On Demand for paid work. The pay rates vary by the demand for specific languages, with more common languages and languages of wealthier countries being paid more. Workers can collaborate with teams working on single videos in small groups. Amara also allows workers to reject tasks they've taken on and decided were too burdensome or uninteresting. 75% of workers are between 18-37, 78%+ hold a bachelor's degree or more, 60%+ are women, and 80% have at least one other source of income. However, 70% of workers do not use other ghost work platforms. The authors express that LeadGenius and Amara are not solely focused on selling labor but also using people's creativity.
RequestersThe authors outline four reasons requesters, ranging from small companies or sole proprieters to large businesses, might seek ghost workers:
- Their own workplaces did not have the expertise for an in-house project.
- Ghost workers are often much faster and cheaper than hiring traditional staff.
- Unexpected spikes in workloads leading to needing an extra worker to take on tasks.
- Ghost workers produce higher quality work than many contractors and even full-time employees, generally due to the rating systems and seeking repeat work.
From Piecework to Outsourcing: A Brief History of Automation's Last MileThis chapter focuses on the history of labor protections for full time workers, and how that has historically left out specific types of workers, and now ghost workers. By examining technological innovation and its ties to labor politics through the late 19th-20th century, the authors discuss divisions around skill labor (what machines can't do) and unskilled labors (contingent labor ideal for automation). They argue that labor advocates focused on making full-time employment in the industrial era protected also made it easier for corporate interests to make piecework, temp work, contract work, and ghost work unskilled and thus unworthy of protections. Such designations of skilled and unskilled largely existed along dominant gender and class lines. They review the history of labor laws, with special attention to The Wagner Act of 1935, largely regarded as the first federal legislation in the US protecting labor rights. They also examine how the holes in the Wagner Act led to loopholes that negatively impact contract workers today.
After reviewing how women during the 1940s worked as human computers were viewed as unskilled and thus temporary, they discuss how in the late 20th century the advent of telecommunications led to a new form of ghost work, outsourcing. Data processing, customer service, and record keeping began to be moved abroad to areas where labor was cheaper and worker protections were looser. They discuss how global politics and Indian politicians led to India becoming the first major epicenter of business process outsourcing (BPOs). They also discuss how tech companies, particularly Microsoft, in the 1980s, became scrutinized for abusing contracting laws to hire "permatemps" - employees classed as temporary, who did the same work as permanent staff, working full time hours, and kept on for multiple years, but for less pay and fewer benefits. Because Microsoft settled out of court on the class-action lawsuit against them, the issue of permatempts has not been legally resolved.
They argue that modern ghost workers are new in that their temp status also makes them indispensible, providing a pool of workers that businesses can go to who have shared experience, availaibility, and diversity almost instantly and around the clock.
Algorithmic Cruelty and the Hidden Costs of Ghost WorkThe authors open with an anecdote of a glitch in 2013 leading to MTurk suspending accounts, and how the suspension caused a participant a loss of $200 in wages, and how she felt it was unfair because it was a problem with MTurk and not her own work. This caused wariness and distrust; when would something like this lead in a loss of primary income? This chapter argues that platforms and their APIs are too rigid to deal with the complexity of hiring, evaluating work, and paying workers. This work (vetting, training, arranging) is referred to as "transaction costs" (Wagner).
In talking to requesters (of ghost workers), they expressed the largest difficulty being matching workers to tasks. Having large amounts of workers to sift through who apply to a task, they need to vet them. Largely platforms provide ratings for requesters to rely on, which many found to not be useful due to manipulation. Further, scores don't transfer across platforms, so it causes workers to have to start from scratch on each platform. Requesters would often prefer to see portfolios or prior work. In training workers, requesters take on that labor, but workers who do poorly aren't paid. The authors argue it is difficult to estavlish trust and accountability in an on-demand space inherently focused on quick labor. Workers also have the ability to "ghost" or abandon projects, which leads requesters to micromanaging workers or monitor them more closely. On demand workers also have to adapt more quickly to the culture of hiring firms, which have different expectations of accountabilities. Another cost absorbed by workers here is equipment and software, which would otherwise be provided by the company they work for. This winds up saving requesters money.
Requests would often mitigate the transactional issues by having a pool of freelance workers they regularly turn to outside of platforms, who they regularly hired on platform. This also saves them money because requesters have to pay the platform. Largely, requesters enjoy on demand work because it is cheap and they can build a quality workforce by trial and error.
Ghost Work's Hidden Pain ScaleThe authors argue that the shift of transaction cost from company to worker can be measured on a pain scale, ranging from annoyance (e.g., lost time seeking/understanding work) to pain (e.g., executing a job without feedback/pay or sense of community). Platforms view their workers as customers who are selling their labor, and so they can decide to go elsewhere and must be accountable to their own choices; the platform does not have the responsibility. The workers on these platforms are not actually workers, but customers who can act as free agents and take on their own risk. This is similar to how YouTube or Facebook views its users as customers, though those customers are the ones keeping the platforms running and making them money.
1-3 on the pain scale: hypervigilance. One of the pains the authors highlight is "hypervigilance." One method of hypervigilance is spending hours waiting through spam/fraud for remote work to find legit work. This is cultural; the authors highlight the history of call centers in India in the 90s exploiting workers leading to a general distrust of gig work. Another method of hypervigilance was being on call at all hours, especially to find work at all. Flexibility, which is touted as the benefit of ghost work, is an illusion, as workers are expected to always be logged into apps/features. The authors argue that its those workers who need online work the most that value flexibility the most, and those that need it least, value it the least (pg. 80).
4-6 on the pain scale: isolation. Workers have little to no guidance or contact with requesters for questions, yet small mistakes can lead to accounts being banned or getting poor reviews. Workers generally only know if they are a good fit for a task once they've taken it on. Even if a requester changes their request halfway through a task, which they are given the power to do, they can still not pay workers or reject them, leading to dings on their reputation score and making workers have to work harder for less to try to make up their score (pg. 83). Workers often have to spend far more time than is worth on tasks because they wind up being more complex than expected and they do not want to damage their reputation. The authors also highlight instances where culture comes into play, like describining panini makers which might not exist often in places like India.
7-10 on the pain scale: not getting paid. Workers can often not be paid due to technical glitches and social chasms between requesters/platforms who picture them having modern equipment to the reality many workers have outdated equipment. Accounts can be suspended for things like moving addresses. The expectation that workers are inherently distrustworthy or trying to game the system leads to good workers being punished by automated suspensions and blocking. Workers trying to create small collections of workers get restricted for example (pg. 88).
Working Hard for (More Than) the MoneyThis chapter deals with why people would choose to do ghost work, given all the risks and drawbacks. One reason is simply having no choice but to seek payment from as many sources as possible, given the rising costs of living and low wages. Even once meeting basic needs, many workers felt it was better than typical jobs in terms of flexibility. Some workers also view it as an opportunity to gain new skills or build a portfolio. The authors highlight that workers in both the US and India shared sentiments of wanting to control their own destinies and be part of a contemporary working world.
When Career Ladders Lost Their RungsThis part of the chapter focuses on how the early 1990s job markets were overcrowded and competitive due to "the GI Bill, post-Jim Crow, and second-wave feminism," leading mostly young college-educated white men into tech. The 1990s and early 200s then saw a surge of venture labor, where people took on high-risk, high-reward jobs in tech for stock options and then cash out early. By the mid-2000s, Gen X and millenial workers entered a workforce that no longer offered the job with benefits, good pay, and longterm sustainability of their parents. Further, well educated people no longer wanted traditional jobs tied to spreadsheets and little agency. Particuarly for low educated and low paid workers, ghost work became a lifeline where good work was impossible to find.
Given so many entry-level jobs no longer become viable careers, but have specific schedules, poor wages, and tied down locations, ghost work is often more appealing. It also allows more historically marginalized communities, genders, and backgrounds to become involved.
The Kindness of Strangers and the Power of CollaborationThe authors discuss how, despite the design of platforms to be isolating and focused on individual work, many workers developed relationships or communities with other workers. Workers overcome the technical barriers to create small communities or communicate with other workers and get to know them. The authors highlight three types of collaboration: (1) reducing overhead costs (e.g., signing up, avoiding scams, finding work); (2) getting work done; (3) create social connections.
The authors also saw cultural differences in collaborations. In India, workers wanted texting and sharing physical space, which also helped with sharing resources and dealing with challenges like translation.
The Double Bottom LinePlatforms can sell both automated software solutions and ghost work human labor solutions. Single bottom line businesses are focused on maximizing profit and do not view their workers as employees, but as customers; labor laws currently allow these companies to treat gig workers as independent contractors. Double bottom line companies (e.g., CloudFactory, per the authors' classifications) sees profit in treating workers like business partners, though this is also based on the companies' intentions and values and not required.
Single bottom lines convert workers into customers. The authors argue that typical on demand platforms use a single bottom line model that "cash[es] in on both sides of the ghost work market" (pg. 144). Alongside the customers using on demand work, workers generate revenue by paying to use the platform. Workers also generate valuable information for the platform, which allows them to automate pieces of the platform. Further, they can sell worker information to advertisers.
Double bottom lines: When platforms view their workers as a workforce rather than customers. The authors call these companies "social enterpreneuriships" which focus on making profits for their investors and also meeting some measurable social welfare goal (benefit corporations / B Corps). These companies view humans in the loop as a labor force and source of work not going away and more than temporary.
The Good Work Code: The Good Work Code created in 2015 at the National Domestic Worker's Alliance focuses on helping tech companies create sustainable on-demand jobs. Companies can sign on to comit to 8 core values and then can use the label in marketing their materials to consumers and workers. http://goodworkcode.org/
However, it is up to companies to choose a more expensive business model in valuing ethics over profit or the single bottom line.