As machine learning and artificial intelligence advances, the fear of technology taking over human jobs increases. A common concern is that much of jobs that required human labor will be replaced by machines, and many people will lose their usefulness in the market. However, in Ghost Works, the sentiment is that as algorithms make certain tasks obsolete, they also create a need for many new tasks that require humans. These tasks are often done internationally, crowdsourced, and fast. Users of technology assume their applications are powered digitally alone, but in fact, many softwares employ this man powered task force in the background to fulfill what artificial intelligence is unable to accomplish. Because of the lack of awareness, and often intentional hiding, of these workers and these gigs, they are referred to as “ghost work.” These labors are being undervalued because of this opacity in their worth and even existence, as a result, ghost workers are underpaid, leading to a growing pool of invisible underclass. 

Though I agree with the idea that new technology will replace antiqued jobs but create new opportunities, what caught my attention while reading was the notion that humans are fundamentally more complex, and different, than artificial intelligence. I disagree with this in a biological and philosophical sense. Most research in psychology and HCI nowadays suggest that humans are better at pattern recognition. Machines, on the other hand, have far superior long term memory storage. Given proper care, memories stored on a drive will not change as time passed, but human memories will fade and be altered by perception and new information. I feel that it is theoretically possible to create an artificial brain that is as powerful as the human brain. However, I agree that human labor may be still necessary on another basis. I think it would be more cost, time, and labor efficient. In a somewhat dystopian view, to create a capable pattern recognition machine through humans, it takes two human laborers and about 17 years of upbringing, costing $233,610, according to data from USDA. This new human will not only be capable of recognizing the difference between hairless puppies and “dick pics”, as the example from the book, but also a wide variety of other tasks. 

Back to the main topic of the reading, I think the main concern is that, as of now, there is a large amount of workers being underpaid for labor that will only grow to be more significant and prevalent. If this trend continues, and the ghost workers continue to be invisible to the general public, more people will lose their jobs as artificial intelligence replaces them without realizing new needs are created elsewhere. Simultaneously, the lack of recognition for the value of these new, machine irreplaceable tasks will cause them to be underpaid and under desired. As of now, demand for these works is much less than the abundant supply of workers all over the globe. However, demand is rapidly growing as machine learning and artificial intelligence advances. 

How do the ideas of this book conflict with the theory that advancing technology may reduce the need for “work” and undesirable tasks and increase opportunities for humans to pursue creative activities?

Much of ghost work is considered “mindless” gigs. How can we make these tasks less mundane and undesirable? 

Can we apply the techniques used to make ghost work “easy” or mindless, for example segmenting tasks and crowdsourcing, to other works to make them easier as well? 

Have ghost work always existed or only recently came into being with the rise of AI? What tasks in the past were underpaid and opaque? How are they similar or different?

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What is Ghost work?

Under the shadow of emerging AI technology and its applications, millions of workers find tasks on online platforms and earn their paycheck by contributing in “human computation”.

These tasks connect end users and machine as an “API”. The work process is totally invisible to the users, yet their contribution is vital to user experience on all kinds of services that rely on the blooming of artificial intelligence. These workers are completely ignored by the majority of users, and their effort are credited to the algorithms and models. The result is, robots are overrated on their performance and capability.  As new software being put into use and the improvement of AI, ghost workers will be assigned to further tasks towards the realization of automation. This is the Paradox of automation’s last mile.

Are they all mindless and unworthy of mention?

I’ve been thinking about the smartness of apps on my phone. Every time I talked about something that can be purchased, soon after that I receive notification of purchase promotion about that product from many apps from all my electronic devices. I know Google, Amazon and Apple probably already projected a prediction on my user activity and preference, however, I’m surprised at their speed of self-confirmation. Is it just my history and the model that determine what to show me next, or there’s something I couldn’t see or imagine?

When we make phone calls only by landline, operators are necessary workforce, and we know their importance. It’s not the most decent occupation, but many people use this job to make ends meet. Today, most people are enjoying the all kinds of service brought to them by fast Internet and computing power, and they are unaware of the people that glue their interaction with the machines.

It’s unfair if we only give credit to the silent, formatted models and neglect the significant contribution of ghost workers’ creativity and flexibility. Just as other collaborate work, effort is effort. Human computation is already an indispensable part of the whole picture. Without their effort, I guess all the expectation on AI will be disappointed in some way.

The jobs (or the combination of tasks) are described as mindless, which means no specific skill is needed to satisfy the goal. As I tried to register on a crowdsourcing website – Figure Eight, it’s not hard to get started with all the helpful “quiz” and tips along your tasks.

Question

1. Even with this book and other research that expose Ghost work to public, will these “irrelevant” information draw their attention on this topic?
2. What else, similar to Ghost work, silently exist in the shadow of a great invention, research breakthrough, and technology?
3. Current law, social status, is it a good status quo? Is there something we can make progress on? Do they demand more “employee” benefit? (Compared to traditional in-office full-time employees)

From class

They are hidden deliberately to hide some shortcoming of the platform. So it’s difficult to decide whether we should expose them.

Are we creating jobs to fire other people?

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Gray and Suri’s 2019 book Ghost Work: How to Stop Silicon Valley from Building a New Global Underclass draws on a five-year study to investigate the booming yet still largely hidden workforce, ghost work, referring to the broad groups who are captioning photos, designing/coding a project, flagging/removing inappropriate content, and so forth. This new working style will continue to displace the traditional 9-to-5 schedule with on-demand tasks. By examining four different ghost work platforms, including MTurk, UHRS, LeadGenius, and Amara.org, it shows that ghost work is in high demand at the current stage of AI development and the data from the crowdsourcing is important to achieve a higher level of automation.

To be honest, I have heard about the ghost work platforms before but never have any experience with them. Through the real-life stories shared by a bunch of ghost workers in the book, I see both sides of the job shifting. From the worker perspective, the advantages of the new work style provide them with the flexibility of the working schedule. One of the ghost workers mentioned that she needed to take care of her mom so she quit her job and chose to remotely work in flexible hours for MTurk. The downsides are the workers are not compensated well nor have any insurance or other benefits. As more and more people shift to the work style, competition for tasks cannot be avoided. I wonder if the competition would intensity the issue of unfair compensation because those who most need the work may be willing to lower the price they ask to grasp the opportunity. As the ghost work industry growing to a certain level, rules and regulations are indeed necessary to ensure the healthy operation of different platforms.

From the ghost work itself perspective, I feel the majority of the current tasks on the platforms such as manually labeling are due to the fact that we are still at the baby stage of AI. Sometimes we are over-optimistic about the current AI achievement and expect AI can solve any problem in our daily life. For one thing, many of the tasks tackled by AI are still naive. For example, on image classification, it takes an individual less than 1 second to get the correct answer, however, machine learning models may suffer from insufficient data or unbalanced distribution between training and testing data and perform poorly in the generalization between different data sets. For another thing, merely relying on the results generated by AI, especially black-box models, to make crucial decisions may put our society at great risk. The trust and reliable AI can hardly be achieved without an understanding of the features learned in a model or the rationale beyond merely producing results. As AI moved to a more advanced stage, the major tasks of crowdsourcing may witness a big change as well. As the tasks completed through human-computer interaction are utilized to push the limit of AI, here are some questions that may be worth further discussion.

• What is the long term goal of the “AI revolution”, is leveraging human source to make AI more AI or utilizing AI to free humans to make humans more human?
• As AI replaces more and more work of comparative low-requirements, would the future increasing unbalance need for micro-tasks and macro-tasks cause societal issues?
• Would any bias be introduced in the tasks of the ghost work because of workers’ demographics, like age, gender, educational level, have an impact on the final model/system performance? If so, is there any way to mitigate the bias?
• As more and more jobs and people shift to ghost work, it raises certain issues: How do ghost workers find quality jobs? How do the platforms pay ghost workers fairly?

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