The Pacific Standard magazine has been running a series where academics, business leaders, technologists and labor leaders have contributed to the discussion on the most consequential changes in labor and the future of work. We invite you to read the contributions from members of our SMC family.

“Caring for the Crowdworker going at it alone”   –>Mary L. Gray, one of our senior researchers at the SMC. She is writing a book, with computer scientist Siddharth Suri, on platform economies, digital labor, and the future of work.

In many ways, the assumption that workers no longer need a supportive or collaborative work environment and can act as self-directed actors is a fair one. Plenty of workers figure out how to find a good gig, develop routines for getting work done quickly, even find water-cooler chatter on worker-centered forums. A significant percentage of crowdworkers string together 30 to 50 hours of work, and rely on networks of peer support to maintain this level of productivity. Workers share information about how to sign up for platforms, what jobs to consider, employers to avoid, even how to do certain tasks when the task instructions leave out key details. Indeed, my time with workers shows that the API silently shifts the burden of finding, training, and retaining talent to workers’ shoulders.

“Working for the machine” –> Michael Bernstein, assistant professor of computer science at Stanford University, where he co-directs the Human-Computer Interaction group and is a Robert N. Noyce Family Faculty Scholar. His research focuses on the design of crowdsourcing and social computing systems.

The computer no longer is just our tool for doing work: it is becoming an instrument that gives us work. Online, networked societies have embarked on a massive shift to take work online, and that means an algorithm may be your next boss, or at least be your task matchmaker. Ask an Uber driver, who is told where to be and when by software. Or ask workers on Amazon’s Mechanical Turk marketplace, who execute information tasks for hours a day at piecework rates.

For Uber Drivers, Data is the Boss –> Alex Rosenblat, researcher and technical writer at Data & Society, a New York organization focused on social, cultural, and ethical issues arising from data-centric technological development.

From Uber’s perspective, drivers are a stopgap solution until autonomous vehicles can replace them. The more permanent Uber employees—the data scientists—algorithmically scrutinize the drivers’ movements to determine where they should be positioned to meet passenger demand. At Uber, drivers are also data points on a screen. The data they generate as they do their work feeds Uber’s surge pricing algorithm, can help determine how long it should take a driver to complete a trip, or could be used to move into markets beyond passenger delivery.

What isn’t counted, counts –> Karen Levy, postdoctoral fellow at New York University School of Law and the Data and Society Research Institute.

Consider long-haul truckers. Most are paid according to the number of miles they drive, which are increasingly tracked by their employers via GPS-enabled “fleet management systems.” What these systems don’t track (and what drivers aren’t paid for) is the time they spend on other essentials—like safety inspections, paperwork, and waiting for hours while their freight is loaded or unloaded at crowded terminals. But because their work is measured by miles driven instead of by some other metric (say, by number of hours worked), truckers have incentives to cut corners—hurrying their safety checks, speeding, ignoring the legally mandated rest breaks meant to keep the highways safe.