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Introduction
Predictions about job loss, automation, and the future of the workplace are frequently based on macro-level factors, including labor markets, job classifications, and work activities. The large-scale findings frequently do not account for the latencies and gaps in artificial intelligence systems, which cannot wholly function without human aid. Shestakofsky uses the firm AllDone to highlight how human labor will not be lost but will instead work alongside technology. His in-depth analysis of the startups evolution illustrates how AI systems continue to rely on human talents and complementary types of emotional labor. The author also digs into cases where human labor is preferable to computer labor for cost-cutting and strategic reasons.
Main body
AllDone was a software business that aimed to alter local service marketing by creating a website that connected customers and sellers of minor services ranging from drapery to construction and cleaning work. When AllDone was in its early stages of development and trying to recruit customers for its system, it depended on the services of a Filipino contract crew to collect information, identify potential users, and run a digital marketing campaign (Shestakofsky, 2017). When AllDones software developers lacked the resources to create and develop an automation process, the team of employees performed what the writer referred to as computational labor. After establishing a consumer base, AllDone focused on sourcing sellers for the platform.
Most vendors were small businesses or individuals who needed to comprehend the systems architecture or guidelines and frequently expressed irritation about a lack of answers to estimates. AllDone engaged a staff of customer service representatives who would carefully explain the system to new vendors and provide recommendations on how to boost their profile to fill this knowledge gap (Shestakofsky, 2017). When AllDone reached its third phase, when it tried to collect more earnings from the sellers and users, it depended on emotional labor to persuade users to renew their subscriptions and computational labor to discourage sellers from evading and gaming the new restrictions.
The author attributes AllDones inner dynamism to its ability to adapt to new business obstacles and create new interrelations between humans and robots. Machine systems rough edges will always necessitate the addition of human labor to smooth them down. Even with AI technologies, software programmers frequently relied on employees to swiftly collect information or try out improvements. Personal interaction was also required to build a loyal consumer base. While more types of economic interaction are expected to be mediated by technology, crucial emotional abilities such as persuasion, support, and empathy still need to be improved.
The author attributes AllDones inner dynamism to its ability to adapt to new business obstacles and create new interrelations between humans and robots. Machine systems rough edges will always necessitate the addition of human labor to smooth them down (Shestakofsky, 2017). Even with AI technologies, software programmers frequently relied on employees to swiftly collect information or try out improvements. Personal interaction was also required to build a loyal consumer base. While more types of economic interaction are expected to be mediated by technology, crucial emotional abilities such as persuasion, encouragement, and empathy still need to be improved.
Conclusion
The ladies who headed the phone hotline support staff performed the emotional labor of counseling potential vendors and calming clients. Women have traditionally been stereotyped as caregivers and support workers. These beliefs are mirrored in technology and the tech sector, as seen by the prevalence of female sounds in the virtual house and telephone assistants. Nevertheless, Shestakoskys article gives intriguing insights into how we conceive human-machine relationships and the future of labor. Instead of worrying about human employees being replaced by machines, we may focus on guaranteeing the healthy development of human-machine interrelations and preventing disadvantaged populations from carrying the burden of emotional and unpaid labor.
Reference
Shestakofsky, B. (2017). Working Algorithms: Software Automation and the Future of Work. Work and Occupations, 44(4), 376-23. Web.
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