{"id":102,"date":"2020-01-28T23:32:21","date_gmt":"2020-01-28T23:32:21","guid":{"rendered":"http:\/\/wordpress.cs.vt.edu\/cs6724s20\/?p=102"},"modified":"2020-01-28T23:32:22","modified_gmt":"2020-01-28T23:32:22","slug":"01-22-2020-palakh-mignonne-jude-ghost-work","status":"publish","type":"post","link":"https:\/\/wordpress.cs.vt.edu\/cs6724s20\/2020\/01\/28\/01-22-2020-palakh-mignonne-jude-ghost-work\/","title":{"rendered":"01\/22\/2020 | PALAKH MIGNONNE JUDE | GHOST WORK"},"content":{"rendered":"\n

SUMMARY<\/h2>\n\n\n\n

\u2018Ghost work\u2019 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 \u2018humans-in-the-loop\u2019 contributing their creativity, innovation, and rational judgement to cases where machines are unable to make decisions accurately. The importance of these \u2018gigs\u2019 has increased exponentially over the past few years \u2013 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\u2019s Mechanical Turk, Microsoft\u2019s UHRS, LeapGenius, and Amara \u2013 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 \u2018human\u2019 behind the seemingly interchangeable worker represented by a pre-defined worked ID.<\/p>\n\n\n\n

REFLECTION<\/h2>\n\n\n\n

As a researcher working with Machine Learning, I think that I often forget the \u2018human-side\u2019 of Machine Learning \u2013 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 \u2018gold standard\u2019. 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.<\/p>\n\n\n\n

The reading reminded me of another paper entitled \u2018Dirty Jobs: The Role of Freelance Labor in Web Service Abuse\u2019 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 \u2018requesters\u2019 and the moderation of the type of tasks that are posted on such ghost work platforms. <\/p>\n\n\n\n

QUESTIONS<\/h2>\n\n\n\n