AI military weapons should be ban

© 2019 Center for Security Studies (CSS), ETH Zurich 1

ETH Zurich
CSSCSS Analyses in Security Policy

No. 251, October 2019, Editor: Fabien Merz

AI in Military Enabling
Applications
The public debate over military use of artificial intelligence (AI) mainly
revolves around autonomous weapons systems. Looking beyond the
specific ethical and political considerations associated with that issue,
there are important questions relating to the organizational,
technical, and functional integration of AI-enabled systems that
determine the balance between potential benefits and risks.

By Niklas Masuhr

In the summer of 2017, Russian President
Vladimir Putin argued that the nation that
had a leading edge in the sphere of artificial
intelligence (AI) would be able to “rule the
world”. This statement, like other similarly
worded contributions to public debates,
suggested a unitary technology with revo-
lutionary impact, similar to the atom bomb.
The reality, however, is much more com-
plex. Apt analogies would be the introduc-
tion of electric power or the rise of the in-
ternet – technical achievements that have
influenced all spheres of human life in
manifold and often contradictory ways.

Probably the best-known subcategory of
AI is machine learning. Technical break-
throughs in computing power, especially in
terms of processors and video cards, have
facilitated rapid progress in this field. Ex-
amples of civilian applications based on
these developments include automatic im-
age recognition, and natural language pro-
cessing, as well as artificial “players” of
board or computer games. In principle,
these programs require multiple compo-
nents. Machine learning-enabled software
must first be trained by experts using –
preferably large – datasets. As a civilian ex-
ample, in order to identify road users, cam-
era images are used as training data. This
enables algorithms to generate predictions
independently in relation to as-yet un-
known data and, ideally, to autonomously
improve their own performance over time.

Already today, some of these software algo-
rithms are capable of surpassing human
talent in their respective areas.

Nevertheless, it is important to note that
existing machine learning applications
have so far only been able to improve their
capabilities in a relatively narrow field, that
is, they become more efficient at solving
existing tasks rather than tapping into new
tasks on their own. This potential for sim-

plifying processes and making them more
efficient is what makes AI a key priority for
armed forces and intelligence services –
which is particularly viewed with skepti-
cism in democratic, liberal societies. Most
public debate is focused on advances in the
autonomization of weapons platforms in
the air, on land and on and under the sea
that can attack targets independently. It
should be pointed out, however, that ad-
vances in machine learning methods are

Joint Operations Command Center in Qatar during the invasion of Iraq in March 2003. Advanced
software and AI can massively reduce the personnel numbers of such staff units. Tim Aubry / Reuters

© 2019 Center for Security Studies (CSS), ETH Zurich 2

CSS Analyses in Security Policy No. 251, October 2019

not sufficient on their own; rather, the fu-
ture of autonomous weapons systems will
also depend on developments in other ar-
eas such as sensors and robotics.

Moreover, the impact of AI developments
is felt across a broad range of routine op-
erations within armed forces, amongst
which the use of autonomous weapons sys-
tems is only one of many elements. Ac-
cordingly, this analysis will focus on certain
aspects of military use of AI that have hith-
erto received less public attention, specifi-
cally those where machine learning meth-
ods will play a role – or are already playing
a role today. In the following, we will look
at a cross-section of issues that illustrate
the complexity and diversity of the range of
topics involved. To this end, the analysis
will first focus on potential outcomes at the
level of strategic decisionmaking. Subse-
quently, it will consider the possible impli-
cations of machine learning for the train-
ing and organization of armed forces.
Finally, it will point out some inferences at
the level of military operations.

Strategic Decisionmaking
AI has the potential to support analysis by
actors ranging from top-level political de-
cision-makers all the way down to infantry
soldiers in the field. This section will focus
on the former sphere, i.e., the political-
strategic ‘brain’ of a national security archi-
tecture. Here, AI-enabled systems could,
for example, predict the behavior of foreign

states and societies, predefine policy op-
tions, or generate highly complex simula-
tions relating to ongoing crises in real time.
The core advantage of machine learning in
this context is that it facilitates greater pre-
cision and can complement human assess-
ments and predictions, which may always
be clouded by emotions and biases. More-
over, in principle it can vastly accelerate de-
cisionmaking processes by enabling gov-
ernments to understand and analyze
situations much quicker than before.

At the same time, even machine learning
cannot guarantee the absence of biases or
analytical errors. Such issues have already
manifested themselves in the civilian sec-
tor, since the heuristic framework demar-

cated in the “training phase” can, for exam-
ple, distort the gathering and categorization
of data that ultimately enables AI to carry
out autonomous analyses. Thus, it has be-
come apparent that the reliability of facial
recognition software varies depending on
the target’s ethnicity. In the intelligence
and military spheres, such issues could have
grave consequences if immature systems
are deployed and trusted. If we consider the
use of AI for decisionmaking in connection
with a “Cuban Missile Crisis”-type scenar-
io, the problems associated with use of
these new technologies become apparent.
Even assuming that it is possible to calcu-
late options for action and potential crisis
scenarios with a high degree of precision,
the possibility remains that the accelera-
tion of decisionmaking may contribute to
the escalation, rather than the de-escala-
tion, of such a crisis, since the actors would
see their respective windows of opportuni-
ty shrinking.

An already complex situation would be-
come even more critical as soon as multiple
states or actors have proprietary intelligent
support programs at their disposal. For one,
multiple AI systems that have been trained
in different ways might come to contrary
conclusions. It would thus be wrong to
think that they could generate outcomes
based on perfect rationality. Moreover,
even high-performance algorithms are not
immune to being misled by fairly tradition-
al means of espionage and deception. For

instance, it is conceivable that
AI might mistakenly assess cer-
tain patterns of behavior as in-
nocuous if they occur often
enough without entailing the
feared outcomes – even assum-
ing that the data base can be-
come much more finely granu-
lated than has hitherto been the

case. Of course, such issues are especially
concerning if AI-enabled analyses are giv-
en a great deal of credence or if it is impos-
sible to verify the validity of their recom-
mendations.

This is precisely where a potentially major
problem becomes apparent: AI-generated
analyses and inferences could gain an over-
sized degree of authority in political deci-
sions. In essence, it is difficult to judge from
an external viewpoint how precise or trust-
worthy an AI-generated assessment really
is. Though it is true that similar problems
also arise where no intelligent software is
used, there is a real chance that the tech-
nology may be used as an exclusive “oracle”
in public debate by governments or corpo-

rations. The question of who exactly has ac-
cess to AI, and thus, who is in a position to
contextualize and interpret its results, is
therefore of utmost importance for society
at large. In democracies, civil-military ten-
sions may be exacerbated if, for example,
the armed forces have sole access to ana-
lytical AI that recommends certain mili-
tary options for action, based on simula-
tions. But even within a security apparatus
as such, access to AI systems and the impli-
cations for actors’ authority may be prob-
lematic, depending on how and where the
AI is embedded in existing institutional
decisionmaking processes and hierarchies.
It is conceivable, for example, that different
ministries or military commands may be
provided with divergent results and recom-
mendations. The situation is further aggra-
vated by the fact that more complex AI, in
particular, may be capable of predicting or
at least predefining scenarios, without the
underlying logic, considerations, and pri-
oritizations necessarily being comprehen-
sible. Such issues, from the dangers of im-
mature AI to the power relations within
and between governments and societies,
illustrate the importance of first embed-
ding AI in a political and institutional con-
text to minimize serious risks. Thus, certain
safeguard mechanisms are required at the
strategic decisionmaking level.

Training and Organization
The problems described above at the level
of strategic decisionmaking are similarly
applicable to the organization and training
of armed forces themselves. One of the
most interesting aspects of machine learn-
ing in this context relates to the education,
training, and selection of military enlisted
and officer personnel. Much like in the ci-
vilian sphere, AI can be used here to create
and continuously update personalized cur-
ricula. For instance, depending on the stu-
dent’s learning style, it could decide to ex-
plain a concept in terms of mathematical
formulae, visualizations, or sports analo-
gies. Within military structures, AI could
ensure that promotions and postings are
carried out more objectively, based on an
improved ability to assess candidates in a
holistic manner.

Another advantage is seen in the potential
ability to design virtual or real-life exercises
in a more realistic or challenging manner,
allowing commanders and staff officers to
prepare better for combat operations – in
particular with a view to engaging with
“enemies” who are capable of thinking dy-
namically. By using intelligent algorithms
either to “play” the roles of adversaries and

AI has the potential to
support analysis by actors
ranging from top-level political
decision-makers all the way
down to infantry soldiers.

© 2019 Center for Security Studies (CSS), ETH Zurich 3

CSS Analyses in Security Policy No. 251, October 2019

populations or to conduct more finely
grained analyses, new operational concepts
and tactics could be developed indepen-
dently of personal and institutional experi-
ence. Moreover, through highly complex
simulations, AI can help to predict the best
ways to use new technologies and integrate
them into existing systems. Especially in
combination with advances in virtual real-
ity (VR), complex algorithms are expected
to considerably improve the realism of tac-
tical training. In addition to AI’s huge po-
tential for military education and training,
it should not be forgotten that even with
“intelligent” syllabi and assessments, the
heuristic framework is in the first instance
defined by human programmers and ana-
lysts. Therefore, a lack of objectivity in
terms of military or personal criteria will
potentially be reflected in algorithms. The
same applies to the value of intelligent sim-
ulations, maneuvers, and wargames: Their
results may not replicate the realities of a
certain theater of operations or scenario –
or they may be given inordinate degrees of
credence.

Other issues arising in connection with
training and education relate to the chang-
ing nature of military careers and profes-
sional pathways as a result of increasing use
of – and thus dependence on – artificial in-
telligence. Armed forces are already con-
fronted with cultural issues in the context
of cyberspace, since they are compelled to
recruit people whose interests and qualifi-
cations do not necessarily match the tradi-
tional self-perception or external image of
the military. It is likely that similar prob-
lems will occur in the context of militarily
harnessing AI. Specifically, the question
arises whether AI specialists serving in
military headquarters should even be re-
quired to undergo basic infantry training
and to which extent military standards of
physical fitness should apply to such re-
cruits. This debate is already underway in
the US, polemically reduced to the short-
hand notion of “blue-haired soldiers”. As
part of these ‘lateral entry’ schemes, expert
civilians are inducted into the forces and
ranked from the start as officers or non-
commissioned officers, which is viewed as
problematic within the Army and Marine
Corps in particular. Nevertheless, it is un-
clear how armed forces will be able to com-
pete with multinational technology corpo-
rations for young talent if they continue to
insist on basic infantry training and a tradi-
tional military organizational culture. The
problems in connection with AI described
above, especially those relating to the cor-
rect interpretation and weighting of the re-

sults it generates, can only be offset by hir-
ing personnel with specialized skills.
Nevertheless, concerns that lateral hires
and varying physical standards based on
specializations might create a caste system
within the armed forces should not be dis-
regarded.

Military Operations
Generally, the assumption is that AI will
support armed forces in collecting, catego-
rizing, and analyzing data more quickly
and efficiently than is currently possible.
For example, current cooperation between
ground and air forces is often hampered by
different data processing systems and ap-
plications requiring manual harmonization
of data. AI-enabled systems can, for exam-
ple, assist with collecting images or signals
collected by drones and categorizing and
transmitting them according to the re-
quirements of multiple recipients. Thus, a
reconnaissance drone’s data could be trans-
mitted in real time to a frontline artillery
unit as well as to an HQ intelligence cell
without requiring time-consuming “trans-
lations” at various interfaces.

Furthermore, intelligent software could
also relieve human operators on the ground
in terms of essential communications tasks,
for instance by automatically switching be-
tween radio frequencies to prevent inter-

ception or jamming. The question here,
though, is how a centralized analytical sys-
tem whose strength lies in the
amalgamation of very diverse information
would be able to cope with the failure of
individual sensors, i.e., whether such a
failure would lead to a complete system
crash or potentially fatal diagnostic errors.
Other examples of applications in this area
include programs to support radar or sonar
reconnaissance, which make it easier to
detect and localize potential targets. Russia
in particular seems to be investing heavily
in machine learning to bolster its integrated
air defense system. Ultimately, AI could
help to significantly degrade the edge of
supposedly “invisible” platforms such as
nuclear submarines and stealth aircraft. In
certain situations, this could negatively im-
pact the strategic stability between the nu-
clear superpowers.

One area where machine learning and re-
lated applications are likely to have a sig-
nificant impact is in connection with staff
work, which has so far accounted for a
great deal of personnel resources and time
expenditure. Such work involves, for exam-
ple, planning for patrols or reconnaissance
flights, administrative tasks, and logistical
organization. Reductions in the sizes of
staffs and headquarters are not only based
on cost considerations, but also on military

Benefits and Potential Advantages Disadvantages and Risks
Strategic
Decisionmaking

– More precise, faster situation
assessments and analyses

– Offsetting emotions and prejudices
– Rational behavior in crisis

situations

– Low crisis stability due to
acceleration of decisions

– Prejudices can be inherent in
algorithms

– Problems regarding the balance
of power within states, for
example between the military
and the civilian leadership.

Training and
Organization of
Armed Forces

– Personalized training, fair
assessments and promotions

– More realistic exercises, maneuvers
and simulations

– Credible simulations of future
technologies and their applications

– Overestimation of AI-generated
results

– Cultural and personnel problems
due to incompatibility between
military culture and values held
by specialized personnel

– Military cast system due to
higher technical specialization

Military Operations – More efficient processing of data
from different sources

– Reduction of administrative and
staff work through forward-looking
logistics

– Reduced risks for troops through
autonomous logistics

– Improvement of support and
reconnaissance systems

– Potential dependencies that
cannot be replaced in the field

– Risks in supply chains due to lack
of inventories and reserves

– Unclear whether autonomous
vehicles can be used in complex
scenarios

– Reduction of strategic stability

Advantages and Disadvantages of AI in the Military Field

CSS Analyses in Security Policy No. 251, October 2019

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necessity, especially in connection with the
resurgence of great power rivalry and its at-
tendant deployment scenarios. Faced with
modern sensors and standoff weapons,
Western armed forces can no longer rely on
expansive bases and installations as they
did in Afghanistan. Beyond staff work, the
potential of machine learning for military
logistics seems especially promising. For
instance, the US Air Force has already in-
troduced “predictive logistics”
for several fleets of aircraft
types, i.e., the intelligent calcu-
lation of repair and mainte-
nance tasks. This allows com-
manders to assign tasks and
targeted maintenance intervals
to individual aircraft much
more efficiently than was hitherto possible.
Unsurprisingly, the latest generation of
fighter aircraft operated by the US and its
allies (especially the F-22 Raptor and the
F-35 Lightning II) are equipped with spe-
cific internal sensors and analytical soft-
ware designed to make full use of these lo-
gistical advantages. Of course, as
highlighted in particular during the debate
over the F-35, there are certain risks in-
volved in excessive dependence on AI ap-
plications. As with the concept of “just-in-
time” operations in the private sector, such
reliance may prove precarious if unforeseen
events should imperil the integrity of the
supply chain.

Further potential benefits accrue from the
automation of transport vehicles. The
advantages here are in the areas of efficiency
gains as well as force protection. On the
one hand, fewer personnel would be needed
to transport equipment and supplies over

long distances; on the other, soldiers (or
private contractors) would be less exposed
to ambushes. Especially during the early
phase of the occupation of Iraq in 2003,
this was a major issue for US forces, despite
their massive military superiority and
modern technology. The idea of automated
resupply doubtlessly has great potential
and already seems to be at a fairly advanced
stage of development. Thus, the US Army

aims to deploy AI-supported trucks by
2020 for operations in convoys in which
only the lead vehicle is manned (Expedient
Leader-Follower). Nevertheless, the success
of such systems depends not only on
developments in the field of machine
learning, but also on fully developed
robotics and sensors. However, once again,
this raises the specter of potentially
premature introduction of systems and
technological dependencies in key military
functions or in too complex scenarios.
While it is certainly desirable to put fewer
personnel in harm’s way on convoys
through contested territory, one may ques-
tion, for example, whether and when au-
tonomous vehicles will be able to operate
in a conflict scenario within a major city.

AI as an Enabling Technology
The effects of AI and machine learning on
the military and the future of warfare can-
not be credibly predicted in terms of a few

succinct keywords or uniform trends. AI is
best understood as a cluster of enabling
technologies that will be applied to most
aspects of the military sphere. Even if tech-
nological progress accelerates, the systems
and platforms powered by it will not be ab-
sorbed simultaneously or with equal effec-
tiveness and efficiency into the technical
arsenal of the armed forces. Neither does it
make sense to view AI as an isolated tech-
nology, given its manifold interactions with
other technological fields, which make con-
sistent and generalized predictions difficult.
Accordingly, it is hard to agree with the
statement by Russian President Vladimir
Putin that mastery of AI implies political
hegemony: Alongside the technological
component, the organizational and political
context must also be taken into account. For
instance, the idea that AI will automatically
imbue a dysfunctional security apparatus
with objectivity and the capacity for rapid
decisionmaking is wishful thinking. More-
over, in military operations, dependency on
AI must be carefully calibrated. For armed
forces in particular, the challenge lies in de-
ciding to what extent and how quickly tra-
ditional, historically evolved organizational
structures and doctrines should be replaced
by new, technology-centric concepts – a
challenge for which military-technical his-
tory offers no clear answer.

Niklas Masuhr is researcher in the Global Security
Team at the Center for Security Studies (CSS) at
ETH Zurich. Among other things, he is the author
of “Lessons of the War in Ukraine for Western
Military Strategy”.

AI is best understood as a cluster
of enabling technologies that
will be applied to most aspects
of the military sphere.

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