=Paper=
{{Paper
|id=Vol-2503/paper1_10
|storemode=property
|title=Toward Usable Theories for Human-Automation Systems
|pdfUrl=https://ceur-ws.org/Vol-2503/paper1_10.pdf
|volume=Vol-2503
|authors=Stéphane Chatty,José C. Campos,Michael D.Harrison,Ovidiu-Andrei Schipor,Radu-Daniel Vatavu,Fabio Paternò,Marco Manca,Carmen Santoro
|dblpUrl=https://dblp.org/rec/conf/eics/Chatty19
}}
==Toward Usable Theories for Human-Automation Systems==
https://ceur-ws.org/Vol-2503/paper1_10.pdf
Toward Usable Theories
for Human-Automation Systems
Stéphane Chatty
Université de Toulouse- ENAC, 31055 Toulouse, France chatty@enac.fr
Abstract. A number of the engineering challenges raised by interactive software
in the last decades have been addressed or worked around. When planning the de-
velopment of large systems, user interfaces are often considered a solved problem
and resources go elsewhere. However, it can be argued that some of the currently
identified challenges (role of humans in large systems, explainability of AI) are
reformulations of problems that are well known to the community of interactive
systems engineering. We attempt here to distinguish which problems have been
addressed by empirical methods and which remain to be solved by scientifical
methods. We then propose requirements for theoretical models that will allow
engineers to go beyond empiricism and increase their control over the systems
they design and develop: engineers need models that consistently describe socio-
cyber-physical systems, that are usable to address practical design questions, and
that allow to automate part of the design and verification processes.
1 Introduction
For most engineers, user interaction is a solved problem. In the 1990s research in
human-computer interaction has conceptualized various new technologies: augmented
reality, virtual reality, ubiquitous computing, touch-based interaction, multimodal in-
teraction, etc. In the 2000s it was unclear to the research community why these tech-
nologies were not more used industrially; a reasonable hypothesis was that engineering
costs were too high and this was a good driver for research in the engineering of in-
teractive computing systems. Then a succession of industrial successes have changed
all assumptions: smartphones, tablets and web-based user interfaces have created vi-
brant ecosystems where hundreds of thousands of quality user interfaces are created on
a regular basis. Designing and building user interfaces is a popular set of skills. R&D
departments are turning to new challenges, mostly those raised by machine learning
techniques.
Mission accomplished? Should the research community turn to new challenges as
well? This article attempts to discern the remaining challenges behind the successes
and to understand how the new engineering challenges raised by artificial intelligence
converge with them. It advocates for a broader perspective on the engineering of human-
computer systems, so as to propose solutions to industries who are facing the challenges
of designing efficient and reliable human-automation systems.
With this perspective, the new challenges are higher than ever. They require the
creation of a scientific body of knowlegde that systems engineers can use to describe
and design hybrid systems made of humans, computers, and social systems such as
rules and procedures.
Copyright © 2019 for this paper by its authors. Use permitted under Creative
Commons License Attribution 4.0 International (CC BY 4.0).
2 S. Chatty
2 HCI Engineering as a succession of challenges
Engineering disciplines are not self-driven: their goals are influenced by what the in-
dustry needs, and ultimately what society wants. In the case of human-computer in-
teraction several technological waves can be identified, each corresponding to a new
breakthrough of the HCI community and each revealing a new set of questions for en-
gineering researchers.
Although it can be argued that job control languages were a user interface that is
worth studying, the literature traces the first user interface engineering questions to
command line interfaces. This is where questions of software architecture appeared:
how can programmers organise their code in a manageable way, how can they manage
the functional core and the user interface in relative independence?
Then came the largest wave, with the introduction of graphical displays. Not only
did it reinforce the architecture challenges, it also raised questions of expressive power:
how can a programmer or a researcher create a new interaction style such as iconic in-
teraction, gesture interaction, etc? This set of challenges amplified as other interaction
paradigms were introduced: information visualization, augmented reality, multimodal-
ity, etc.
The following wave came immediately behind the second. Now that the power of
expression was growing, the expectations were growing too and came the serious engi-
neering questions: what user interface for what use, what skills to design them, what de-
sign and engineering processes, how does one check that a design is satisfactory? These
questions introduced a degree of confusion because very diverse phenomena and very
diverse expertises are involved: software development issues, graphical issues, human
behavior issues, design method issues. There still are relatively independent research
communities who address engineering issues and sometimes reinvent solutions: human
factors, programming languages, user interface design, user interface engineering, de-
sign thinking, and probably others.
More discreetely, the next wave was that of the safety-critical industries, and par-
ticularly aeronautics. Not only do their engineers have all of the above challenges, they
also need to display a total control of the requirements of their systems and to imple-
ment a very demanding safety culture: one type of failure must never occur twice in
the whole industry, and only unknown types of accidents are tolerated. The control of
requirements is very demanding in terms of engineering processes, and the safety cul-
ture is very demanding in terms of scientific models so as to understand the causes of
accidents and avoid their repetition.
3 The success of empiricism and brute force
Although no spectacular success has been recorded in engineering research, comparable
to touch-based interaction and speech queries, the challenges raised by most of these
successive waves have gradually faded. Most theoretical questions, including archi-
tecture, expressive power and model-based systems engineering are still not perfectly
addressed. However, the progressive introduction of new constructions in programming
languages have led to admissible practical solutions for architecture. The use of vari-
ous programming patterns and Domain Specific Languages have made the expressivity
Toward Usable Theories for Human-Automation Systems 3
sufficient for a wide range of programmers to develop the interaction styles they de-
sire. Participatory design methods and prototyping techniques have become widespread
and have joined the wider movements of agile programming, design thinking and lean
startup. Now even business schools teach methods than can be applied to interactive
product design with reasonable success.
In practice, these successes are in large part those of empiricism and brute force.
What the evolution of programming languages have permitted, particularly with Web
technologies, is the recruitment of dozens of thousands of programmers with a shal-
low computing science background and still the abilty to produce useful code. This
brute-force solution is perhaps not the success expected by researchers, nor the most
efficient possible solution, but it is an effective solution. Similarly, the popularization
of agile methods and design thinking derives from the implicit acceptance that no the-
ory provides complete solutions for designing systems. Instead, every case is treated by
gathering empirical data and iterating through approximative designs until finding an
acceptable one.
Although very remote from design thinking, the current popularity of machine
learning techniques is the culmination of this series of industrial progresses based on
empiricism and brute force: software is expected to obtain acceptable results by itself
through sheer statistics, without even having to gather more than mere examples.
4 From empiricism to theories
This combination of pragmatic approaches is an objective success: it is revolutionizing
the whole field, including the engineering of safety-critical systems. But it is only a
stage in the evolution of our field. Other engineering fields have gone through empirical
phases before becoming science-based. For instance, architecture and civil engineering
have been an empirical discipline long before structural analysis has become a modern
science in the early 19th century. When this finally happened, creating buildings became
more efficient in two ways: it required less building materials because smaller safety
margins were required, and it required less manpower because computations required
less skills.
This is where the remaining challenges from the previous technological waves com-
bine with the new challenges coming with the new wave. They all highlight the need of
better support for engineers who are responsible for the predictability of systems that
combine humans and automation.
4.1 The remaining challenges
Safety-critical systems provide good examples of the remaining challenges. The hypo-
thetical cause of the recent Boeing 737 MAX accidents is such an example. To start
with, it is not satisfying that engineers can create conditions where users have no clue
of what is happening, no knowledge of the existence of the malfunctioning subsystem,
and no way of acting on it; it does not reflect full control of the whole human-computer
systems by the engineers who design it. It is not satisfying either that certification ex-
perts were not able to detect the defective approach. The human-automation system that
4 S. Chatty
they build and certify is apparently too complex for humans equipped with the available
analytical frameworks. But what is worse is that comparable conditions had contributed
to the AF447 accident ten years before. It seems that, in this field, the aeronautical sec-
tor is so ill equipped with conceptual frameworks that it does not learn efficiently from
previous accidents.
Another example is the difficulties encountered in producing safety-critical software
in air traffic control. Applying software assurance methods is slow, leaving enough time
for changes in requirements to appear, thus requiring a new cycle of software assurance,
and the whole process sometimes diverge.
4.2 The new technological wave
As said previously, most engineers consider that the user interface is a solved problem.
There is little reason in ignoring this, even though we may have more efficient solutions
in mind. Where the remaining issues are is where the behaviour of the whole human-
computer system is studied, not only that of the user interface. Currently, this concerns
the engineers of safety-critical systems who must eradicate so-called “human error”;
tomorrow, it may be have a wider audience and even create opportunities to propose
new solutions for interactive software itself.
With this perspective, machine-learning techniques are the new technological wave
in human-automation systems. And the questions it raises are similar to the challenges
above: being able to ensure that users will understand why an algorithm has reached a
given state (“explainable AI”) is the most cited issue, followed by the certifiability of
AI-based software. In the future, being able to reason on human-automation systems
may even become a long-term public issue; many of the questions asked today by the
press to AI researchers about the consequences of AI on sociéty might be answered
more efficiently by HCI engineers with the appropriate theoretical framework to reason
on the behaviour of human-automation systems.
Toward Usable Theories for Human-Automation Systems 5
4.3 Getting rid of extra-theoretical analyses
All of these point at the deficiencies of the available theoretical frameworks, that leave a
lot to extra-theoretical considerations. For instance, although it seems within our reach
to represent the user’s belief on the state of an interactive software in the same the-
oretical framework as the state itself, engineers still are condemned to using common
sense for that. There are theories for software, theories for hardware, theories for human
behaviour, but no common ground that allow to combine them.
Even when focusing on homogeneous system, notably software, there is another
area where extra-theoretical considerations are required today: the verification of sys-
tem properties. With few exceptions, the properties that can be checked within the
bounds of theoretical systems are of very limited practical use: they do not correspond
to the kind of questions that are asked by engineers, or the kind of invariants they need
to check.
We propose the following common goal to all researchers in HCI engineering: defin-
ing the theories that rid us of extra-theoretical reasoning when describing, designing,
programming and verifying human-automation systems. In our perspective, this is how
model-based systems engineering will become of practical use to engineers.
5 Defining theories of human-computer systems
The challenges and the research program described above are far-reaching. Before be-
ing able to build formal theories, the research community will probably need to build
ontologies that cover human-automation systems. Process-based ontologies are good
candidates, but other options are probably as good. It is only equipped with a working
ontology that candidate theories can be produced, tested and compared.
We propose here three requirements for such theories of human-automation sys-
tems. An acceptable theory must:
– provide consistent models for socio-cyber-physical systems; human behaviours,
physical objects, and social rules must be interoperable and interchangeable within
the theory.
– must be usable in practice to capture real designs and real design questions, includ-
ing coding issues, so as to gain adoption from engineers and give them back control
of their complex systems.
– must be usable to automate design processes and property verification, so as to gain
adoption from industries, whose decisions are more and more based on immediate
benefits.
In these requirements, “usability” can be understood in two meanings: the com-
mon sense meaning (the theory must “work”), but also the HCI meaning (they must
be designed for ease of use by engineers). Not only should engineers be able to apply
model-based engineering instead of extra-theoretical reasonings, they should also be
able to do it while understanding at all steps what is happening. In the current situation,
engineering often stop where ‘human factors” start because engineers feel out of their
depth in that area. But what the evolution of aviation safety teaches us is that this creates
an area of improperly attributed responsibilities, that can lead to safety incidents, very
slow engineering processes, or both.
6 S. Chatty
6 Conclusion
The current successes of the digital industry force researchers on interactive systems
engineering to reconsider their research directions. The heavy investments on machine
learning techniques may even reduce available resources for their research. However,
we have attempted to demonstrate here that there actually is a consistent set of engi-
neering challenges in HCI engineering and AI engineering that point toward a common
scientific problem: the creation of consistent and usable theories of human-automation
systems, that can be used to describe and predict the behavior of these hybrid systems
so that engineers can more effectively and efficiently exert control over them.
7 Acknowledgements
The author is director of the innovation programme at DSNA, 50 rue Henry Farman,
Paris, France but this work is done as an associate researcher at ENAC. This work does
not necessarily reflect the views of DSNA.