interaction: The automated routines
must eventually be resumed after
a certain time span or through an
explicit interaction. A real and
dramatic example in which this
binary solution (automation on or off )
fails is the switching off of automated,
distance-based braking systems
in trucks. Drivers who switch off
distance braking and forget to switch
it back on again regularly cause
accidents on highways. Similarly, one
can discuss the semantics of manual
For intervening interaction, it has
to be clear whether a change is short
term (e.g., steering changes in how I
drive to work today) or is meant as a
configuration (e.g., my workplace has
GOLDEN RULES AND
Ben Shneiderman’s golden rules
ben/ goldenrules.html) have proven
over the years to be a substantial
orientation point for user interface
design. Since the underlying
concepts still have great relevance,
we discuss them in the context of
autonomous systems. We suggest
some reconsiderations to understand
the differences that are related to the
paradigm of intervention.
Strive for consistency. Consistency
requires the same or similar
sequences of actions in similar
situations. Since fine-grained
control and actions are drastically
reduced in automated systems, the
consistency of system behavior
should be perceptible across different
The rule for autonomous systems
is: Strive for dynamic and contextual
consistency and ensure expectability
and predictability. Deviations
from expected behavior (e.g., an
autonomous car always takes the
same route) should be understandable
and explainable. If inconsistency is
perceived, intervention must be offered.
Enable frequent users to use
shortcuts. Shortcuts make frequent
and repetitive actions more efficient.
Automation already removes needs
for frequent actions and explicit
interaction becomes an exception.
Thus we propose: Replace the
need for frequent explicit actions
and interventions by automation.
services. Those cases have in common
that sensors and actors are included,
complex components are connected
in a network, and data is continuously
produced and triggers machine-learning-based improvement.
Autonomous cars are part of a traffic
system, exchanging data between
participating components and
sensors. Interventions can be on
different levels, including a stop at a
coffee shop, the demand for a detour
to pick someone up, or changing
priorities (e.g., speed versus fuel
The interaction design for
interventions raises the questions
of how options are visible and how
consequences are comprehensible.
One option for intervention interfaces
in the car may be to stick with the
steering wheel and pedals. However,
what would the semantics for
intervention be using these controls?
Using the brake pedal to signal a need
to stop, tilting the steering wheel for
a change in direction, and hitting
the accelerator pedal to increase
speed? In what temporal way would
this intervention be combined with
the autonomous behavior? What
does it mean if users stop hitting
the accelerator pedal? Does it mean
that they want to reduce speed or
that they intend to switch back
to the automated speed control?
These problems point to an essential
characteristic of intervention
For intervening interaction, it has to be
clear whether a change is short term
(e.g., changes in how I drive to work today)
or is meant as a configuration (e.g., my
workplace has permanently changed).
Intervention user interfaces are inspired by other reflections on interaction. What the
concepts of implicit [ 2], incidental [ 3], and proactive interaction [ 4] have in common is
that they support autonomous behavior by observing the changes of a system’s context,
partially with sensors. These concepts are relevant in ubiquitous computing, autonomous
vehicles and traffic systems, and automated manufacturing ( https://en.wikipedia.org/wiki/
Industry_ 4.0), where the role of humans is challenged by transferring control to systems
and distributing and decentralizing decisions between various components.
Concepts such as intervening use [ 5] build a contrast with usage for routine tasks and
emphasize the interdependency between intervention and explorability. Intervenability
as a means of privacy protection [ 6] emphasizes the modification of personal datasets and
profiles of users.
Autonomous vehicles in networked traffic systems: Control can be temporarily handed over
from the vehicle to the driver.
Smart homes and ambient assisted living: Implicit interaction based on context (e.g., motion or
infrared sensors); control of heating, ventilation, and air conditioning.
Automated manufacturing and smart factories: Predefined maintenance intervals can be
adjusted if urgent orders have to be dealt with.
Personal assistance with wearable and mobile devices: They offer sensor-based data tracking
and contextual awareness.
Machine learning and cognitive services: Interventions can influence the sets of data and
profiles aimed at improving the service used.