SUMMARY
‘Ghost work’ is a new world of employment that encompasses the work done behind-the-scenes by various unnamed online workers that help to power mobile apps, websites, and AI systems. These workers are the ‘humans-in-the-loop’ contributing their creativity, innovation, and rational judgement to cases where machines are unable to make decisions accurately. The importance of these ‘gigs’ has increased exponentially over the past few years – especially in order to provide better training data for Machine Learning systems (such as the ImageNet challenge that involved annotating millions of images), cleaning-up social media pages (ensuring that there is little to no abusive content), as well as providing accurate descriptions for product and restaurant reviews. This work covers various online platforms like Amazon’s Mechanical Turk, Microsoft’s UHRS, LeapGenius, and Amara – each catering to slightly varied tasks ranging from micro-tasks (tasks that can be done quickly, but require many people) to macro-tasks (larger projects such as copyediting a newsletter, linking video to captions). The work also illustrates the lives of four such ghost workers, and their experiences on these platforms, giving some insight into the ‘human’ behind the seemingly interchangeable worker represented by a pre-defined worked ID.
REFLECTION
As a researcher working with Machine Learning, I think that I often forget the ‘human-side’ of Machine Learning – especially with respect to the generation of labels for supervised tasks. I found the description about the lives of Joan, Kala, Zaffar, and Karen to be particularly interesting and insightful. This was interesting to me as it helped me better understand the motivation of people engaging in crowd work and helped me better appreciate the efforts that need to be taken in order to gain a good ‘gold standard’. I also found it interesting to learn that on-demand workers produce higher-quality work when compared to the quality of work produced by full-time employees. While the reading posits that a potential reason for this is the competitive nature of on-demand jobs, I wonder if incentive-based bonuses in the case of full-time employees could have an impact on the quality of work provided by these employees.
The reading reminded me of another paper entitled ‘Dirty Jobs: The Role of Freelance Labor in Web Service Abuse’ which focusses on freelance work done via Freelancer.com. This paper discusses the abuse of various web services including solving CAPTCHAs, Online Social Networks Linking, etc. This reading talks about vetting processes done for the workers, but it made me wonder about the type of vetting done for ‘requesters’ and the moderation of the type of tasks that are posted on such ghost work platforms.
QUESTIONS
- What is the main motivation for ghost workers with a Master’s degree to work on these platforms? Is it generally due to an ailing family member? Additionally, Amara has a larger percentage of women, is there any reason behind this?
- In the case of a platform such as Amara, where workers perform tasks in teams, how do they handle cases of harassment (if any were to occur)? Are they any policies that exist to deal with such situations?
- Is there any form of moderation of the type of tasks that are posted on these sights? Are the ghost workers allowed to flag tasks that might seem to contribute to web service abuse? (For example, multiple account creation by using ghost workers to solve CAPTCHAs in real-time).
