Summary
In the Introduction, the authors define Ghost Work as the massive manual human labour that supports Artificial Intelligence (AI) systems’ ability to provide seamless user experience. It also describes a study that the authors conducted to analyze the humans involved in the Ghost Work. The section provides examples of such workers and their working situation/condition. They also discuss the lack of legal policies or definitions to incorporate such workers into the employment laws.
Additionally, the authors discuss the changing employment environment where different companies need similar low-skilled work. However, the work availability fluctuates on a project and so a platforms that pool readily-available low-skilled workers form a necessary supplement to the work-force.
Chapter 1 discusses examples of some platforms that act as rendezvous points for the work-force and companies that require instant labour. It starts off with the example of Amazon’s Mechanical Turk, the pioneer work-sharing platform. It moves on to discuss a case-study of ImageNet, a labour-intensive data annotation project and explains ghost workers’ role in the progress of the entire AI community. It also gives examples of other platforms like UHRS, LeadGenius, Amara and UpWork. The examples display the contrast and similarities between Micro (pieces of projects) and Macro (entire projects/sub-projects) Ghost Work. It also shows the difference in platform’s conduct towards its workers.
Reflection
Ghost work is a very important aspect of current AI methods. The human intuition has been developed over a number of generations. Currently, AI solves tasks that are very basic to humans. But, as it develops the complexity of tasks will increase exponentially. As mentioned, for sentiment analysis, the system relies on input human emotions to determine the sentiment of a sentence. However, recent research need sentiment analysis of human expressions (more complex for humans). This shows a trend with diminishing profits. Currently, the AI problems needs expertise that 99% of humans possess. But complexity will bring this number down to a point where a few in the world will possess such knowledge. Hence, the current architecture of recruiting ghost workers will no longer strive.
Another point is the rate of this complexity. If it is high, then we need insignificant changes in employment policies. However, if the trend is going to continue for a significant amount of time, then we need immediate policies to avoid worker abuse. The ghost workers’ contributions are momentous. However, due to their massive numbers and relative low-skill requirement, the returns are significantly curtailed. Unionization works as a solution to this problem. The concept was initially formed for this very purpose. Unions will have the power to fight against the massive corporations for fair policies and compensation for their valuable labour. A short example is given in Chapter 1, where CrowdFlower’s workers filed a suit against the company for labor practices. The platforms need to be responsible for the employees because that is the source of their compensation (like Uber is responsible for its drivers).
Questions
- Will there be a point when AI will not need any human input? Will there be a point when we exhaust all human intelligence and intuition?
- Can we put the workers in an existing employment classification like contract labourers?
- How can we quantify the exact profit of Ghost Work for the original contractors? Can we utilize this to appropriately compensate the workers?
- Where do the similarities between Ghost Work and contract labourers end?
- Can we determine the area of expertise that needs to be developed for more such work? Or is it based on necessity?
