The Dull, Dirty, and Dangerous Truth About Robotics

For years, robotics has relied on the terms 'dull, dirty, and dangerous' to describe tasks where robots might be useful, taking over work that's undesirable for humans. A classic example of such a job is 'repetitive physical labor on a steaming hot factory floor involving heavy machinery that threatens life and limb.' But what exactly makes a task dull, dirty, or dangerous? And who makes that assumption?

Researchers from the RAI Institute recently published a paper tackling these questions and proposing a framework to help roboticists understand the job context for their technology. The team conducted an empirical analysis of robotics publications between 1980 and 2024 that mention DDD and found that only 2.7% define DDD and only 8.7% provide examples of tasks or jobs. The definitions vary, and many examples aren't particularly specific, such as 'industrial manufacturing' or 'home care.' The researchers then reviewed social science literature in anthropology, economics, political science, psychology, and sociology to develop better definitions for dull, dirty, and dangerous work.

They found that social, economic, and cultural factors play a significant role in determining what work fits into these categories. When it comes to 'dangerous' work, the researchers note that occupational injuries tend to be underreported, with some studies estimating up to 70% of cases missing in administrative databases. Additionally, injuries and risk factors are rarely disaggregated by characteristics like gender, migration status, formal/informal employment, and work activities.

The concept of 'dirty work' is also more complex than it seems. While it may involve physical dirtiness, it's also about stigma. Socially tainted jobs are often servile or involve interacting with stigmatized groups, and morally tainted jobs include tasks that people commonly perceive as sinful, deceptive, or otherwise defying norms of civility.

The researchers propose a framework that takes into account the worker's perspective and the context of the job. They suggest gathering key pieces of information to reflect on what physical or social aspects of the task are dull, dirty, or dangerous. The framework also emphasizes awareness of context, including the physical and social environment of an occupation and industry that can influence the DDD nature of a task.

The team's corresponding worksheet suggests existing data sources to draw on and encourages seeking out multiple perspectives and considering potential sources of bias in the information. As an example, they looked at the waste and recycling industry, which seems like a job that hits all the Ds. However, many workers take pride in providing this essential service, and the job has aspects that make it not dull, such as day-to-day interaction with coworkers and task variety.