In 1983, Lisanne Bainbridge wrote "Ironies of Automation" about che...
Automatica,
Vol. 19, No. 6. pp. 775 779, 1983
Printed in Great Britain.
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Pergamon Press Ltd.
© 1983 International Federation of Automatic Control
Brief Paper
Ironies of Automation*
LISANNE BAINBRIDGEt
Key Words--Control engineering computer applications; man-machine systems; on-line operation;
process control; system failure and recovery.
Abstract--This paper discusses the ways in which automation of
industrial processes may expand rather than eliminate problems
with the human operator. Some comments will be made on
methods of alleviating these problems within the "classic'
approach of leaving the operator with responsibility for
abnormal conditions, and on the potential for continued use of
the human operator for on-line decision-making within
human-computer collaboration.
Irony:
combination of circumstances, the result of which is the
direct opposite of what might be expected.
Paradox:
seemingly absurd though perhaps really well-founded
statement.
THE classic aim of automation is to replace human manual
control, planning and problem solving by automatic devices and
computers. However, as Bibby and colleagues (1975) point out:
"even highly automated systems, such as electric power networks,
need human beings for supervision, adjustment, main.tenance,
expansion and improvement. Therefore one can draw the
paradoxical conclusion that automated systems still are
man-machine systems, for which both technical and human
factors are important." This paper suggests that the increased
interest in human factors among engineers reflects the irony that
the more advanced a control system is, so the more crucial may be
the contribution of the human operator.
This paper is particularly concerned with control in process
industries, although examples will be drawn from flight-deck
automation. In process plants the different modes of operation
may be automated to different extents, for example normal
operation and shut-down may be atomatic while start-up and
abnormal conditions are manual. The problems of the use of
automatic or manual control are a function of the predictability
of process behaviour, whatever the mode of operation. The first
two sections of this paper discuss automatic on-line control
where a human operator is expected to take-over in abnormal
conditions, the last section introduces some aspects of human-
computer collaboration in on-line control.
1. Introduction
The important ironies of the classic approach to automation
lie in the expectations of the system designers, and in the nature of
the tasks left for the human operators to carry out.
The designer's view of the human operator may be that the
operator is unreliable and inefficient, so should be eliminated
from the system. There are two ironies of this attitude. One is that
*Received 16 December 1982; revised 23 May 1983. The
original version of this paper was presented at the
IFAC/IFIP/IFORS/IEA Conference on Analysis, Design, and
Evaluation of Man-Machine Systems which was held in Baden-
Baden, F.R.G. during September 1982. The published proceed-
ings of this IFAC meeting may be ordered from Pergamon Press
Ltd, Headington Hill Hall, Oxford OX3 0BW, U.K. This paper
was recommended for publication in revised form by editor A.
Sage.
t Department of Psychology, University College London,
London WC1E 6BT, U.K.
775
designer errors can be a major source of operating problems.
Unfortunately people who have collected data on this are
reluctant to publish them, as the actual figures are difficult to
interpret. (Some types of error may be reported more readily than
others, and there may be disagreement about their origin.) The
second irony is that the designer who tries to eliminate the
operator still leaves the operator to do the tasks which the
designer cannot think how to automate. It is this approach which
causes the problems to be discussed here, as it means that the
operator can be left with an arbitrary collection of tasks, and little
thought may have been given to providing support for them.
1.1.
Tasks after automation.
There are two general categories
of task left for an operator in an automated system. He may be
expected to monitor that the automatic system is operating
correctly, and if it is not he may be expected to call a more
experienced operator or to take-over himself. We will discuss the
ironies of manual take-over first, as the points made also have
implications for monitoring. To take over and stabilize the
process requires manual control skills, to diagnose the fault as a
basis for shut down or recovery requires cognitive skills.
1.1.1.
Manual control skills.
Several studies (Edwards and Lees,
1974) have shown the difference between inexperienced and
experienced process operators making a step change. The
experienced operator makes the minimum number of actions,
and the process output moves smoothly and quickly to the new
level, while with an inexperienced operator it oscillates round the
target value. Unfortunately, physical skills deteriorate when they
are not used, particularly the refinements of gain and timing. This
means that a formerly experienced operator who has been
monitoring an automated process may now be an inexperienced
one. If he takes over he may set the process into oscillation. He
may have to wait for feedback, rather than controlling by open-
loop, and it will be difficult for him to interpret whether the
feedback shows that there is something wrong with the system or
more simply that he has misjudged his control action. He will
need to make actions to counteract his ineffective control, which
will add to his work load. When manual take-over is needed there
is likely to be something wrong with the process, so that unusual
actions will be needed to control it, and one can argue that the
operator needs to be more rather than less skilled, and less rather
than more loaded, than average.
1.1.2.
Cognitive skills.
Long-term knowledge:
An operator who finds out how to
control the plant for himself, without explicit training, uses a set
of propositions about possible process behaviour, from which he
generates strategies to try (e.g. Bainbridge, 1981). Similarly an
operator will only be able to generate successful new strategies for
unusual situations if he has an adequate knowledge of the
process. There are two problems with this for 'machine-minding'
operators. One is that efficient retrieval of knowledge from long-
term memory depends on frequency of use (consider any subject
which you passed an examination in at school and have not
thought about since). The other is that this type of knowledge
develops only through use and feedback about its effectiveness.
People given this knowledge in theoretical classroom instruction
without appropriate practical exercises will probably not
understand much of it, as it will not be within a framework which
776 Brief Paper
makes it meaningful, and they will not remember much of it as it
will not be associated with retrieval strategies which are
integrated with the rest of the task. There is some concern that the
present generation of automated systems, which are monitored
by former manual operators, are riding on their skills, which later
generations of operators cannot be expected to have.
Working storage:
The other important aspect of cognitive
skills in on-line decision making is that decisions are made within
the context of the operator's knowledge of the current state of the
process. This is a more complex form of running memory than the
notion of a limited capacity short-term store used for items such
as telephone numbers. The operator has in his head (Bainbridge,
1975) not raw data about the process state, but results of making
predictions and decisions about the process which will be useful
in future situations, including his future actions. This information
takes time to build up. Manual operators may come into the
control room quarter to half an hour before they are due to take
over control, so they can get this feel for what the process is doing.
The implication of this for manual take-over from automatically
controlled plant is that the operator who has to do something
quickly can only do so on the basis of minimum information, he
will not be able to make decisions based on wide knowledge of the
plant state until he has had time to check and think about it.
1.1.3
Monitoring.
It may seem that the operator who is expected
solely to monitor that the automatics are acting correctly, and to
call the supervisor if they are not, has a relatively simple task
which does not raise the above complexities. One complexity
which it does raise of course is that the supervisor too will not be
able to take-over if he has not been reviewing his relevant
knowledge, or practising a crucial manual skill. Another problem
arises when one asks whether monitoring can be done by an
unskilled operator.
We know from many 'vigilance" studies (Mackworth, 1950)
that it is impossible for even a highly motivated human being to
maintain effective visual attention towards a source of
information on which very little happens, for more than about
half an hour. This means that it is humanly impossible to carry
out the basic function of monitoring for unlikely abnormalities~
which therefore has to be done by an automatic alarm system
connected to sound signals. (Manual operators will notice
abnormal behaviour of variables which they look at as part of
their control task, but may be equally poor at noticing changes
on others.) This raises the question of who notices when the alarm
system is not working properly. Again, the operator will not
monitor the automatics effectively if they have been operating
acceptably for a long period. A classic method of enforcing
operator attention to a steady-state system is to require him to
make a log, Unfortunately people can write down numbers
without noticing what they are.
A more serious irony is that the automatic control system has
been put in because it can do the job better than the operator, but
yet the operator is being asked to monitor that it is working
effectively. There are two types of problem with this. In complex
modes of operation the monitor needs to know what the corrcct
behaviour of the process should be, for example in batch
processes where the variables have to follow a particular
trajectory in time. Such knowledge requires either special
training or special displays.
The second problem is that if the decisions can be fully
specified then a computer can make them more quickly, taking
into account more dimensions and using more accurately
specified criteria than a human operator can. There is therefore
no way in which the human operator can check in real-time that
the computer is following its rules correctly. One can therefore
only expect the operator to monitor the computer's decisions at
some meta-level, to decide whether the computer's decisions are
'acceptable', If the computer is being used to make the decisions
because human judgement and intuitive reasoning are not
adequate in this context, then which of the decisions is to be
accepted ? The human monitor has been given an impossible task.
1.2. Operator attitudes.
I know of one automated plant where
the management had to be present during the night shift, or the
operators switched the process to 'manual'. This raises general
issues about the importance of skill to the individual. One result
of skill is that the operator knows he can take-over adequately if
required. Otherwise the job is one of the worst types, it is very
boring but very responsible, yet there is no opportunity to aquire
or maintain the qualities required to handle the responsibility.
The level of skill that a worker has is also a major aspect of his
status, both within and outside the working community. If the job
is 'deskilled' by being reduced to monitoring, this is difficult for
the individuals involved to come to terms with. It also leads to the
ironies of incongruous pay differentials, when the deskilled
workers insist on a high pay level as the remaining symbol of a
status which is no longer justified by the job content.
Ekkers and colleagues (1979) have published a preliminary
study of the correlations between control system characteristics
and the operators' subjective health and feeling of achievement.
To greatly simplify: high coherence of process information, high
process complexity and high process controllability (whether
manual or by adequate automatics) were all associated with low
levels of stress and workload and good health, and the inverse,
while fast process dynamics and a high frequency of actions
which cannot be made directly on the interface were associated
with high stress and workload and poor health. High process
controllability, good interface ergonomics and a rich pattern of
activities were all associated with high feeling of achievement.
Many studies show that high levels of stress lead to errors, whitc
poor health and low job satisfaction lead to the high indirect costs
of absenteeism, etc. (e.g. Mobley and colleagues, 1979i.
2. Approaches to solutions
One might state these problems as a paradox, that by
automating the process the human operator is given a task which
is only possible for someone who is in on-line control. This
section will discuss some possible solutions to problems of
maintaining the efficiency and skills of the operator if he is
expected to monitor and take over control; the next section will
introduce recent proposals for keeping the human operator on-
line with computer support.
Solving these problems involves very multi-dimensional
decision making: suggestions for discussion will be made here.
The recommendations in any particular case will depend on such
factors as process size and complexity, the rate of process change,
the speed and frequency of process or automatic control failure,
the variability of the product and the environment, the simplicity
and cost of shut down, and the qualities of the operator.
2.1.
Monitoring.
In any situation where a low probability
event must be noticed quickly then the operator must be given
artificial assistance, if necessary even alarms on alarms. In a
process with a large number of loops there is no way in which the
human operator can get quickly to the correct part of the plant
without alarms, preferably also some form of alarm analysis.
Unfortunately a proliferation of flashing red lights will confuse
rather than help. There are major problems and ironies in the
design of large alarm systems for the human operator
(Rasmussen and Rouse, 1981).
Displays can help the operator to monitor automatic control
performance, by showing the target values. This is simple for
single tolerance bands, but becomes more complex if tolerances
change throughout batch processing. One possible solution is to
show the currently appropriate tolerances on a VDU by software
generation. This does not actually get round the problems, but
only raises the same ones in a different form. The operator will
not watch the VDU if there is a very low probability of the
computer control failing. If the computer can generate the
required values then it should also be able to do the monitoring
and alarms. And how does the operator monitor that the
computer is working correctly, or take over if it obviously is not'?
Major problems may be raised for an operator who is highly
practised at using computer generated displays if these are no
longer available in an emergency. One ironic but sensible
suggestion is that direct wired displays should be used for the
main process information, and software displays for quantitative
detail (Jervis and Pope, 1977).
'Catastrophic' breaks to failure are relatively easy to identify.
Unfortunately automatic control can 'camouflage' system failure
by controlling against the variable changes, so that trends do not
become apparent until they are beyond control. This implies that
the automatics should also monitor unusual variable movement.
"Graceful degradation' of performance is quoted in 'Fitts List's of
Brief Paper 777
man-computer qualities as an advantage of man over machine.
This is not an aspect of human performance to be aimed for in
computers, as it can raise problems with monitoring for failure (e.g.
Wiener and Curry, 1980); automatic systems should fail obviously.
If the-human operator must monitor the details of computer
decision making then, ironically, it is necessary for the computer
to make these decisions using methods and criteria, and at a rate,
which the operator can follow, even when this may not be the
most efficient method technically. If this is not done then when
the operator does not believe or agree with the computer be will
be unable to trace back through the system's decision sequence to
see how far he does agree.
One method of overcoming vigilance problems which is
frequently suggested is to increase the signal rate artificially. It
would be a mistake, however, to increase artificially the rate of
computer failure as the operator will then not trust the system.
Ephrath (1980) has reported a study in which system
performance was worse with computer aiding, because the
operator made the decisions anyway, and checking the computer
added to his workload.
2.2.
Working storage.
If the human operator is not involved in
on-line control he will not have detailed knowledge of the current
state of the system. One can ask what limita~tions this places on
the possibility for effective manual take-over, whether for
stabilization or shut-down of the process, or for fault diagnosis.
The straightforward solution when shut-down is simple and
low-cost is to shut down automatically. The problems arise with
processes which, because of complexity, cost or other factors (e.g.
an aircraft in the air) must be stabilized rather than shut-down.
Should this be done manually or automatically? Manual shut-
down is usable if the process dynamics can be left for several
minutes while the operator works out what is happening. For
very fast failures, within a few seconds (e.g. pressurized water
nuclear reactor rather than an aircraft), when there is no warning
from prior changes so that on-line working storage would also be
useless, then reliable automatic response is necessary, whatever
the investment needed, and if this is not possible then the process
should not be built if the costs of failure are unacceptable.
With less fast failures it may be possible to 'buy time' with
overlearned manual responses. This requires frequent practice on
a high fidelity simulator, and a sufficient understanding of system
failures to be sure that all categories of failure are covered. If
response to failure requires a larger number of separate actions
than can be made in the time available then some must be made
automatically and the remainder by a highly practised operator.
practise solving problems within the known information. It is
inadequate to expect the operator to react to unfamiliar events
solely by consulting operating procedures. These cannot cover all
tl~e possibilities, so the operator is expected to monitor them and
fill in the gaps. However, it is ironic to train operators in following
instructions and then put them in the system to provide
intelligence.
Of course, if there are frequent alarms throughout the day then
the operator will have a large amount of experience of controlling
and thinking about the process as part of his normal work.
Perhaps the final irony is that it is the most successful automated
systems, with rare need for manual intervention, which may need
the greatest investment in human operator training.
3. Human computer collaboration
By taking away the easy parts of his task, automation can
make the difficult parts of the human operator's task more
difficult. Several writers (Wiener and Curry, 1980; Rouse, 1981)
point out that the 'Fitts list' approach to automation, assigning to
man and machine the tasks they are best at, is no longer sufficient.
It does not consider the integration of man and computer, nor
how to maintain the effectiveness of the human operator by
supporting his skills and motivation. There will always be a
substantial human involvement with automated systems,
because criteria other than efficiency are involved, e.g. when the
cost of automating some modes of operation is not justified by
the value of the product, or because the public will not accept
high-risk systems with no human component. This suggests that
methods of human-computer collaboration need to be more
fully developed. Definer (1981) lists the possible levels of human
intervention in automated decision making. This paper will
discuss the possibilities for computer intervention in human
decision making. These include instructing or advising the
operator, mitigating his errors, providing sophisticated displays,
and assisting him when task loads are high. Rouse (1981) calls
these 'covert' human-computer interaction.
3.1.
Instructions and advice.
Using the computer to give
instructions is inappropriate if the operator is simply acting as a
transducer, as the computer could equally well activate a more
reliable one. Thompson (1981) lists four types of advice, about:
underlying causes, relative importance, alternative actions
available, and how to implement actions. When following advice
the operator's reactions will be slower, and less integrated than if he
can generate the sequence of activity himself; and he is getting no
practice in being 'intelligent'. There are also problems with the
efficient display of procedural information.
2.3.
Long-term knowledge.
Points in the previous section make
it clear that it can be important to maintain manual skills. One
possibility is to allow the operator to use hands-on control for a
short period in each shift. If this suggestion is laughable then
simulator practice must be provided. A simulator adequate to
teach the basic behaviour of the process can be very primitive.
Accurate fast reactions can only be learned on a high fidelity
simulator, so if such reactions are necessary then this is a
necessary cost.
Similar points can be made about the cognitive skills of
scheduling and diagnosis. Simple pictorial representations are
adequate for training some types of fault detection (Duncan and
Shepherd, 1975), but only if faults can be identified from the
steady-state appearance of the control panel, and waiting for the
steady-state is acceptable. If fault detection involves identifying
changes over time then dynamic simulators are needed for
training (Marshall and Shepherd, 1981). Simple recognition
training is also not sufficient to develop skills for dealing with
unknown faults or for choosing corrective actions (Duncan,
1981).
There are problems with the use of any simulator to train for
extreme situations. Unknown faults cannot be simulated, and
system behaviour may not be known for faults which can be
predicted but have not been experienced. This means that
training must be concerned with general strategies rather than
specific responses, for example simulations can be used to give
experience with low probability events, which may be known to
the trainer but not to the trainee. No one can be taught about
unknown properties of the system, but they can be taught to
3.2.
Mitigating human error.
Machine possibilities for
counteracting human error range from simple hardware
interlocks to complex on-line computation. Except where specific
sequences of operations must be followed it is more appropriate
to place such 'checks' on the effects of actions, as this does not
make assumptions about the strategy used to reach this effect.
Under manual control human operators often obtain enough
feedback about the results of their actions within a few seconds to
correct their own errors (Ruffell-Smith, 1979), but Wiener and
Curry (1980) give examples of humans making the same types of
errors in setting up and monitoring automatic equipment, when
they do not get adequate feedback. This should perhaps be
designed in. Kreifeldt and McCarthy (1981) give advice about
displays to help operators who have been interrupted in mid-
sequence. Rouse (1981) suggests computer monitoring of human
eye movements to check that instrument scanning is appropriate,
for example to prevent tunnel vision.
3.3.
Software generated displays.
The increasing availability of
soft displays on VDUs raises fascinating possibilities for
designing displays compatible with the specific knowledge and
cognitive processes being used in a task. This has led to such rich
veins of creative speculation that it seems rather mean to point
out that there are difficulties in practice.
One possibility is to display only data relevant to a particular
mode of operation, such as start-up, routine operations, or
maintenance. Care is needed however, as it is possible for an
interface which is ideal for normal conditions to camouflage the
development of abnormal ones (Edwards, 1981).
778 Brief Paper
Goodstein (1981) has discussed process displays which are
compatible with different types of operator skill, using a
classification of three levels of behaviour suggested by
Rasmussen (1979), i.e. skill based, rule based and knowledge
based. The use of different types of skill is partly a function of the
operator's experience though the types probably do not fall on a
simple continuum. Chafin (1981) has discussed how interface
design recommendations depend on whether the operator, is
naive/novice/competent/expert. However, he was concerned with
human access to computer data bases when not under time
pressure. Man machine interaction under time pressure raises
special problems. The change between knowledge-based
thinking and 'reflex' reaction is not solely a function of practice,
but also depends on the uncertainty of the environment, so that
the same task elements may be done using different types of skill
at different times. It could therefore confuse rather than help the
operator to give him a display which is solely a function of his
overall skill level. Non-time-stressed operators, if they find they
have the wrong type of display, might themselves request a
different level of information. This would add to the work load of
someone making decisions which are paced by a dynamic system.
Rouse (1981) has therefore suggested that the computer might
identify which type of skill the operator is using, and change the
displays (he does not say how this might be done), We do not
know how confused operators would be by display changes
which were not under their own control. Ephraph and Young
(1981 ) have commented that it takes time for an operator to shift
between activity modes, e.g. from monitoring to controlling, even
when these are under his control, and one assumes that the same
problems would arise with changes in display mode. Certainly a
great deal of care would be needed to make sure that the different
displays were compatible. Rasmussen and Lind's recent paper
(1981) was about the different levels of abstraction at which the
operator might be thinking about the process, which would
define the knowledge base to be displayed. Again, although
operators evidently do think at different levels of complexity and
abstraction at different times, it is not clear that they would be
able to use, or choose, many different displays under time stress.
Some points were made above about the problems of operators
who have learned to work with computer generated displays,
when these displays are no longer available in abnormal
conditions. Recent research on human memory (Craik, 1979)
suggests that the more processing for meaning that some data has
received the more effectively it is remembered. This makes one
wonder how much the operator will learn about the structure of
the process if information about it is presented so successfully
that he does not have to think about it to take it in. It certainly
would be ironic if we find that the most compatible display is not
the best display to give to the operator after all! (As usual with
display choice decisions this would depend on the task to be
done. A highly compatible display always supports rapid
reactions. These points speculate whether they also support
aquisition of the knowledge and thinking skills needed in
abnormal conditions.)
A few practical points can be suggested. There should be at
least one source of information permanently available for each
type of information which cannot be mapped simply onto others,
e.g. about layout of plant in space as opposed to its functional
topology. Operators should not have to page between displays to
obtain information about abnormal states in parts of the process
other than the one they are currently thinking about, nor between
displays giving information needed within one decision process.
Research on sophisticated displays should concentrate on the
problems of ensuring compatibility between them, rather than
finding which independent display is best for one particular
function without considering its relation to information for other
functions. To end on a more optimistic note, software displays
offer some interesting possibilities for enriching the operator's
task by allowing him to design his own interface.
3.4. Relieving human work-load. A computer can be used to
reduce human work-load either by simplifying the operator's
decisions, as above, or by taking over some of the decision
making, The studies which have been done on this show that it is
a complex issue. Ephrath and Young (1981) found that overall
control performance was better with manual control of a single
loop. but was also better with an autopilot in the complex
environment of a cockpit simulator. This suggests that aiding is
best used at higher work loads. However, the effect of the type of
aiding depends on the type of work-load. Johannsen and Rouse
(1981) found that pilots reported less depth of planning under
autopilot in abnormal environmental conditions, presumably
because the autopilot was dealing with the conditions, but more
planning under emergency aircraft conditions, where they
suggest that the autopilot frees the pilot from on-line control so
he can think about other things. Chu and Rouse (1979) studied a
situation with both computer aiding and autopilot. They
arranged for the computer to take over decision making when the
operator had a queue of one other task item to be dealt with and
he was controlling manually, or after a queue of three items if the
autopilot was controlling. The study by Enstrom and Rouse
(1977) makes it clear why Rouse (1981) comments that more
sophisticated on-line methods of adapting computer aiding to
human work-load will only be possible if the work-load
computations can be done in real time. (It would be rash to claim
it as an irony that the aim of aiding human limited capacity has
pushed computing to the limit of its capacity, as technology has a
way of catching up with such remarks.) Enstrom and Rouse also
make the important point that the human being must know
which tasks the computer is dealing with and how, Otherwise the
same problems arise as in human teams in which there is no clear
allocation of responsibility. Sinaiko (1972) makes a comment
which emphasizes the importance of the human operator's
perception of the computer's abilities: "when loads were light, the
man appeared willing to let the computer carry mosl of the
assignment responsibility; when loads were heavy, the men much
more often stepped in [and] over-rode the computer". Evidently,
quite apart from technical considerations, the design of computer
aiding is a multi-dimensional problem.
4. Conclusion
The ingenious suggestions reviewed in the last section show
that humans working without time-pressure can be impressive
problem solvers. The difficulty remains that they are less effective
when under time pressure. 1 hope this paper has made clear both
the irony that one is not by automating necessarily removing the
difficulties, and also the possibility that resolving them will
require even greater technological ingenuity than does classic
automation.
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In 1983, Lisanne Bainbridge wrote "Ironies of Automation" about chemical plants and flight decks. Nothing to do with software. Predicted everything anyway.
Three ironies hitting AI assisted engineering right now:
1) You're asked to review code generated because the AI is faster than you. Using what deep understanding, exactly?
2) Skills decay without practice. Junior engineers who've never debugged by hand won't start when the agent hallucinates.
3) Whatever can't be automated gets left to the human: deskilled, unsupported, holding the hardest edge cases.