Recently introduced in an NBER working paper, the Stanford Task Network (TaskNet) is an occupational taxonomy that draws its task inventories directly from workers. The project’s interactive platform is available to explore now.
The Lab spoke with PhD student Jennifer Wang and Digital Fellow Andreas Haupt about how TaskNet can help researchers and policymakers.
What is TaskNet?
Jenn Wang: TaskNet is a database of tasks performed in different occupations. It provides task lists for each occupation and describes the characteristics of each task.
Andy Haupt: It also is a suite of tools to “find” tasks. Our just-published paper creates tasks for six occupations—software engineers, lawyers, primary school teachers, emergency medicinephysicians, dishwashers, and personal care aides—based on AI-based interviews.
Why did we need this new taxonomy for work?
Jenn: To understand how AI might change work, we first need a clear picture of what people actually do. Much of the research on AI and jobs starts with existing task lists, so what those lists include—and leave out—changes our findings and interpretation. We wanted to develop a way to learn directly from workers and keep our descriptions of work current as their responsibilities change.
Andy: We have a powerful technology, AI, and work is changing. We might reshuffle who does what, redesign occupations, and what’s done by AI. The units to do such job design are tasks. We have decent information on tasks, but we could do better by using multiple methods: for example interviewers.
What is O*NET, and how does TaskNet complement it?
Andy: The Occupational Information Network (O*NET) is a database of occupational information maintained by the US Department of Labor. Among (many) other things, it provides information on tasks.
Jenn: TaskNet complements it by using interviews to collect detailed accounts of everyday work. Across the six occupations, we identified 79 tasks without an O*NET counterpart and 58 that provide more specific descriptions of existing tasks.
Andy: Missing tasks lead to wrong predictions about jobs, for example on how exposed (meaning how much of an impact AI could have) any given occupation is.
What do interviews with workers reveal that job descriptions or existing classifications might miss?
Andy: There’s not one and only way. Different methods have different strengths: postings are abundant, but they’re written by employers, so show their perspective, and probably not the less exciting parts of work. Experts bring an expert perspective.
Jenn: Workers have firsthand knowledge of what their jobs involve, including responsibilities that may not appear in formal job descriptions. Interviews give them space to describe that work in their own words and let us ask follow-up questions to understand the details. That helps us capture tasks that people might take for granted.
What findings stood out to you?
Jenn: One finding that stands out is how much of everyday work can be invisible in official descriptions of a job. These are tasks that keep work running, like coordinating handoffs, checking the quality of someone else’s work, or helping a colleague learn. Workers may take these responsibilities for granted, even though they require time, judgment, and experience. Making them visible gives us a fuller picture of what a job involves and how AI might affect it.
Andy: Our task aggregation method works, it produces task lists that represent individual worker accounts well. The task lists we find lead to higher exposure estimates, and tasks are often “invisible” in their work. To me, the invisible part was surprising. I had no prior on the impact on exposure