Author
JM Bradshaw
Year
2013
The 7 Deadly Myths of Autonomous Systems
JM. Bradshaw, Hoffman, Johnson, and Woods 2013. (View Paper → )
In this article, we explore some widespread misconceptions surrounding the topic of "autonomous systems."….to set the stage, in this essay we bust some "myths" of autonomy.
The Seven Myths:
- Autonomy is unidimensional. It's a mistake to view autonomy as a single property; instead, real autonomy involves at least two distinct dimensions—self‐sufficiency (the ability to take care of oneself) and self‐directedness (freedom from outside control)—that must be balanced in any human–machine system.
- Levels of autonomy provide a useful roadmap. The idea that machine capabilities can be ordered on a simple scale (from low to high autonomy) oversimplifies the complex, context-dependent nature of tasks, failing to capture the multifaceted interplay of functions in real-world systems.
- Autonomy is a widget. Treating autonomy as a discrete, pluggable component ignores that true autonomous behavior emerges only through integrated interactions between machines, humans, tasks, and the surrounding context.
- Autonomous systems are autonomous. Even when a system is designed to operate 'autonomously,' no machine is truly independent in every situation; all systems have operational limits and often require human oversight or context-specific adjustments.
- Full autonomy eliminates the need for human collaboration. The belief that a fully autonomous system can operate without human involvement overlooks the reality that effective performance in complex, dynamic environments always benefits from—and sometimes depends on—coordinated human–machine teamwork.
- Increased autonomy simply substitutes or multiplies human capabilities. Rather than being a straightforward replacement or amplifier of human work, adding autonomy can fundamentally change how tasks are performed, often introducing new challenges in coordination and interdependence.
- Full autonomy is both possible and always desirable. The notion that achieving full autonomy is the ultimate goal ignores the trade-offs involved—such as stretched system capacities and new forms of complexity—which means that even highly autonomous systems require careful integration with human oversight.