As the power of digital platforms grows, tensions between platform companies and their users are increasingly making headlines. Prominent content creators have been “deplatformed” for spreading misinformation and hate speech. Uber and Lyft drivers frequently organize to resist exploitative policies.
Existing studies of platform governance emphasize how platforms’ rules are set, implemented, and enforced. One strand of research emphasizes the role of algorithms and digital interfaces, which dictate how users interact with content and with each other. Another focuses on the human labor involved in implementing policies pertaining to user-generated content.
Are smart machines coming for our jobs?
In the past, technological change has generally led to the displacement of workers from some jobs, but also to the creation of new work. For example, as automation reduced the number of workers needed to grow and harvest crops in the early 20th century, technological change resulted in employment gains in the manufacturing and service sectors.
Today, however, many worry that the historical link between technological innovation and job creation may be coming to an end.
Big data and artificial intelligence make it possible for computers to perform tasks that previously required complex human cognition. Software algorithms are already driving cars, diagnosing diseases, and writing news articles.
A credible case can be made that, thanks to the rapid development of AI, this wave of technological change will usher in an era of widespread unemployment.
Most contemporary inquiries into the future of work offer projections of employment trends at the level of industries or occupations. These studies are useful for helping us conceptualize broad shifts in labor markets, but they aren’t able to shed light on the complex and unpredictable ways in which human workers and software systems interact in real-world settings.
In a recent study, I argue that in-depth examinations of the organizations in which software algorithms are developed and implemented can help us generate new insights into the question of when software systems function autonomously, and when they rely on the assistance of complementary human workers.