Throwback Thursday: A model for types and levels of automation

This is our second post on our “throwback” series. In this paper, I will take you through an article written by the best in the human factors and ergonomics field, the late Raja Parasuraman, Tom Sheridan, and Chris Wickens. Though several authors have introduced the concept of automation being implemented at various levels, for me this article nailed it.

The key excerpts from this article are highlighted below along with my commentary. Companies chasing automation blindly should keep these points in mind when designing their systems.

Automation is not all or none, but can vary across a continuum of levels, from the lowest level of fully manual performance to the highest level of full automation.
— Parasuraman, Sheridan, & Wickens, pp. 287

This means that between the extremes of a machine offering no assistance to a human to a machine doing everything for  the human, there are other automation design options. For example, the machine can offer a suggestion or implement a suggestion if the human approves or does everything autonomously and then informs the human or does everything autonomously and informs the human when asked. Let's consider the context of driving. In the example below, as we move from 1 to 4, the level of automation increases.

  1. I drive my car to work 
  2. I drive my car, KITT (from the Knight Rider) tells me the fastest route to work but I chose to override its suggestion 
  3. I drive my car, KITT tells me the fastest route to work and does not give me the option to override its suggestion 
  4. KITT plans and drives me to work
Automation can be applied to four broad classes of functions: 1) information acquisition; 2) information analysis; 3) decision and action selection; and 4) action implementation. Within each of these types, automation can be applied across a continuum of levels from low to high, i.e., from fully manual to fully automatic.
— Parasuraman, Sheridan, & Wickens, pp. 286

The way humans process information can be divided into four stages:

  1. information acquisition, which involves sensing data
  2. information analysis which involves making inferences with data
  3. decision and action selection, which involves making decision from among various choices
  4. action implementation, which involves doing the action.

Here are four examples of automation applied at each level:

  1. information acquisition, which involves sensing data
    • Example: night vision goggles enhance external data
  2. information analysis which involves making inferences with data
    • Example: historical graph of MPG in some cars
  3. decision and action selection, which involves making decision from among various choices
    • Example: Google Maps routes to a destination; where it presents 3 possible routes based on different criteria
  4. action implementation, which involves doing the action.
    • Example: automatic stapling in a photocopier

The authors say that automation can be applied to each of these stages of human information processing

An important consideration in deciding upon the type and level of automation in any system design is the evaluation of the consequences for human operator performance in the resulting system.

— Parasuraman, Sheridan, & Wickens, pp. 290

Choosing an automation design without any regard for the strengths and limitations of the human operator or for the characteristics of the environment in which the operator works in (e.g., high stress) is not an effective strategy.  When choosing the degree of automation, it is important to consider the impacts it may have on the operator.

  • How would it affect the operator workload?
  • How would it affect the operator's understanding of the environment (in research we call this situation awareness)?
  • How would it affect the combined operator-machine performance?
  • Would operators over-trust the machine and be unable to overcome automation failures?

It is worth noting that NHTSA's current description of vehicle autonomy (figure) is NOT human-centered and is instead focused on the capabilities and tasks of the machine.

From NHTSA.gov (https://www.nhtsa.gov/technology-innovation/automated-vehicles-safety)

From NHTSA.gov (https://www.nhtsa.gov/technology-innovation/automated-vehicles-safety)

Citation:  Parasuraman, R., Sheridan, T. B., & Wickens, C. (2000). A model for types and levels of human interaction with automation. IEEE Transactions on Systems, Man, and Cybernetics, 30, 286-297.

Downloadable link here.