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UX design in AI
A trustworthy face for the AI brain

October 2020

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Ericsson | UX design in AI

Introduction

Contents
03

AI today04

The key to AI success

05

What is trust?

06

Competence08

Benevolence and openness

10

Integrity

11

Charisma

12

Beyond the building blocks

14

Conclusion

15

Authors and references

Currently, computational capacity is
doubling roughly every 18 months.
The pace of this development, amplified
by rapid improvements in software, hasresulted in artificial intelligence (AI) andadvanced algorithms that are quickly
evolving to understand and interpret someof our most complex natural processes.
At the same time, the ability to accessthis capacity is multiplying due to sharp
increases in bandwidth, improvements
in latency and other quality of service
parameters with technologies such as
5G. Interfaces are also becoming moreseamless due to advances in cloudcomputing as well as visual, tactile
and verbal interface technologies.
These exponential improvements havebrought what, just over a decade ago, wereconsidered industrial-strength processing
and communication capabilities into
the homes and hands of individuals
everywhere. As industries adopt thesetechnologies to modernize and automate
their business processes to increase value
chain efficiency and effectiveness, a newservice-based concept for the technology
has emerged.
The self-driving or autonomous
car is an example of this new concept.
Eventually cars will no longer have drivers,
a fundamental change in the concept ofa car. The passenger of such a vehicle
will interact with it on a much higher andabstract level as a service.

When we apply this concept to the telecom
sector; that is, creating a “self-driving
network”, AI technology will be the brains
behind this change. This presents two
main challenges for those developing theconcept and service:• The conceptual shift from today’sunderstanding of what a network is,
becoming something more abstract
than what it is today, operating on
new parameters• The fact that a user of such a service
will interact with the system on a muchhigher, more abstract level
Therefore, the understanding of thebusiness goals and the user of the system
is key to success.
With the role of users shifting fromdrivers to passengers and from operators
to managers, designers will need to create
highly collaborative solutions allowing
tangible and reliable interaction between
AI technology and the user.
In light of this, our Experience Designteam has been researching and developing
how to design trustworthy, AI-powered
services for telecom operators. Throughdesigning the Cognitive Operation Support
System service concept, we have identified
four components of human trust that canbe applied to AI-powered systems.
These four pillars – competence,
benevolence, integrity and charisma –are the key areas designers and businessowners need to address to be successfulwhen it comes to the adoption of AI.
In this paper, we will share ourexperience of designing a trustworthy,
AI-powered Cognitive Operation Support
System (OSS) service.

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AI today

The current face of AI

AI is an umbrella term encompassing
many different methodologies andconcepts, referring to any machinedeveloped to perform tasks that would
require intelligence if done by a human.1
Although the media commonly portrays
AI capabilities as superior to humancapabilities – that is, as an artificial
super intelligence (ASI) – the truthis quite different. Since the earliest
explorations into the AI field, scientists
and practitioners have sought to create
a computer with a level of intelligencesimilar to a human. Known as an artificial
general intelligence (AGI), these would bemachines with a reasonable degree ofself-understanding and autonomous
self-control, able to solve a variety ofcomplex problems in a variety of contexts.2
Despite the huge advancements of AI,
especially in the last decade, we are still
far away from being able to create an AGI,
let alone an ASI.
The current form of AI we areworking with is known as an Artificial
Narrow Intelligence (ANI), or “weak AI”.
ANI systems are created to carry out
specific tasks showing specific aspects
of intelligence in a specific context.3 All
current applications of AI, whether it isan autonomous car, a chatting app
camera filter, or an intelligent OSS,
are all considered narrow or “weak”
by this definition.
An easier way to describe the roleof current AI applications is to call them“agentive technology”4 – whereby we canthink of them as our assistants or agents,
handling a discreet task and not theentire job.
In this current context, humans still needto have a view of the bigger picture andare still required to supervise, evaluate andorchestrate the work of these AI systems.

1

Minsky, 1982

2

Goertzel and Pennachin, 2005

3

Gobble M. M., 20194

Noessel, 2017

Super AI

General AI

Narrow AI (current state)

Figure 1: Levels of AI

Inside a network operations center

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The key to AI success

Trust as a vital component in AI adoption

There is an increasing trend of digitalassistants appearing in many different
aspects of our lives. Powered by machinelearning (ML) models, they analyze datato come up with statistical probabilities
that can be used to offer recommendations
and make predictions and decisions, fromsuggesting the optimum route to take on acommute, to adjudicating whether we are
viable for a loan or not.
Although they sound less impressive
than the idea of the ASI’s superior artificial
brain, the recommendations, predictions
and decisions taken by the AI systems canbe considered a fundamental change in theway humans are using tools – a paradigm
shift in the human-tool relationship.
Since the beginning of this relationship,
humans have always been in full control
not only of what the tool should do, butalso exactly how it will work, at least in thedesign and creation phase. The progression
to the current status of AI is an evolution ofthis relationship in two ways.
First, it is an upgrade of the tool’s statusfrom the role of a “slave” to that of ”agent”,
giving it agency by having a degree of
autonomy with regards to “what” it should
be doing. And second, it is a change in thatwith AI, we no longer entirely decide “how”
the tool executes its function. In fact, inmany cases, the creators of an AI systemcannot entirely describe the criteria that theML model has used to reach the output. Thisis known as the “Black Box problem”.5

Taking these points into consideration,
the following can be said about the current
state of AI systems:
1. Rather than just executing what thehuman user wants, AI systems will
autonomously come up with predictions,recommendations and decisions.
2. We are not always able to fully
understand or explain why an
AI/ML system has reached its output.
3. AI/ML output is based on statisticalprobabilities just like humandecision-making – it judges low
or high probability of outcomes,
it’s not some kind of ultimate truthor absolute objective correctness.
We can therefore reach the conclusionthat a degree of trust is needed, before
the user can hand responsibility over tothe AI – and give the autonomous car thesteering wheel.6
The requirements in building thistrust-based relationship vary according
to the specific task the AI is supposed tohandle. Accepting an AI’s recommendation
on which movie to watch is much “easier”
with a lower trust threshold than, forexample, the recommendation on which
medicine a doctor should prescribe totheir patient.

In a recent survey7 asking owners of smart
voice assistant devices to list the tasks they
perform using the device, 84.9 percent
reported they use it to set a timer, while only
3.5 percent reported using it to call a cab.
A recent study8 has shown that when it
comes to the application of AI in a business
context, 94 percent of business executives
understand that AI is essential to businessstrategy. However, a separate study by
MIT Solan9 found that only 18 percentof companies are widely adopting andunderstanding AI.
In designing an OSS AI solutionthat takes critical decisions affecting
the performance of an entire network,
we discovered that the success of thesystem depended on more than building
more efficient and accurate models andalgorithms. We came to the realization thattrust is an essential factor in the human-AI
interaction, and if we want the users of our
AI solutions to accept handing over morecritical tasks and decisions, we need todesign them to be trustworthy.

Bathaee, Y., 2017
P Anders et al 2018, Muir, B. M. 1987, Siau, K., & Wang, W., 20187www.experian.com/blogs/insights/2016/09/commerce-is-a-conversation-a-survey-on-amazon-echo-and-voice-assistants/
8
www.perspectives.eiu.com/sites/default/files/EIU_Microsoft%20-%20Intelligent%20Economies_AI%27s%20transformation%20of%20industries%20and%20society.pdf
9www.sloanreview.mit.edu/projects/artificial-intelligence-in-business-gets-real/
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What is trust?

The four components of trust in human relationships

Although the concept of trust in human-AI
relationships is a new field, we don’t haveto work from scratch.
The approach of our Experience Designteam delete was human-centric. Humans
have been trusting other humans since
the beginning of our existence, and once
human-human trust relationships were
established, we started to put our trust
in entities and organizations; religions,political parties, banks, schools, businessand so on. Our approach was to draw on
the formula of trust already functioning

in these human-human and humanorganization relationships, and use it
as a base for building trust in
human-AI interaction.
The four main factors that contributeto building trust in another person orentity are: competence, benevolence andopenness, integrity and charisma.10
A good example to illustrate these four
components in action is the process ofdecision making when hiring an employeethat will be responsible for a task in
an office.

When dealing with “digital” assistants inthe form of AI systems, exactly the same
framework of trust applies.
The following pages examine eachof these components in turn, from theperspective of human-AI interaction,
along with relevant examples ofdesign-related decision and focus areasthat can contribute towards creating atrustworthy AI experience.

Figure 2: The components of trust

01. Competence
They prove they
understand the task andcan do it or have done it
before successfully withsome degree of autonomy.

02. Benevolenceand openness
They show a degreeof flexibility; forexample, they are
accepting of otherpeople’s opinions
and feedback.
Competence

Benevolenceand openness

Integrity

Charisma

03. Integrity
They exhibit behavior inline with a common set ofshared ethical standards,
such as honesty, respect
and a sense of fairness.

10

Stern & Coleman, 2015, Dietz and Den Hartog, 2006, Schoorman et al., 200704. Charisma
How suitable do theysound or look forthe task, and how
attracted are we totheir personality on a
subconscious level.

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Competence

Can you do the job?

In practice within an AI system, the trust
component of “competence” essentiallymeans the system is designed todemonstrate that it is capable of fulfillingthe user’s needs and that it can deliver what
it promises.
Here are some practical examples of
how UX designers and practitioners cancontribute to an AI system’s ability todemonstrate competence:
Explainability
Ensuring the system can communicate
the reason behind its decisions andits confidence in different results andrecommendations in a way that users
can easily understand.

Figure 3: Componentsof trust – competence

Competence

Adoption of AI-powered networks
requires knowing they’re up to
the task.

Benevolenceand openness

Integrity

Charisma

Usefulness
Making sure the system is employing
AI capabilities to fulfill an actual need orsolve a real problem for the users in an
effective way.
Trialability
Giving the users the ability to try the AI
system or test out its recommendations ina quick, safe and controllable way before
they decide to use or approve it.
Demonstration of results
Being able to show evidence that using
the AI system has resulted in an
improved outcome.

Network performance diagnostics

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Figure 4: Cognitive OSS prototype design demonstrating ”Explainability”

For each suggested action, users havea transparent view of the motivations
behind the AI’s decisions

Figure 5: Demonstration of results

In the system’s homepage, users
will have brief KPIs that show how
successful the system is, for example
the improvement in resolved incidents
and business value numbers

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Benevolence and openness

Are you on my side?

An AI demonstrating “benevolence” canbe defined as a system designed to makedecisions in the user’s best interest, andto communicate the intentions behinddecisions to the human user. It should also
show flexibility, acceptance of change
and new input – exactly as you would
expect from a new human colleague. Some
practical examples of how UX designers
can contribute to the benevolence andopenness of an AI system are:Controllability
Providing an easy way for the user tointervene and change, undo or dismiss
an action or decision taken by the AI,
as well as the ability to feed their own
recommendations into the system.

Figure 6: Components of trust –benevolence and openness

Competence

Benevolenceand openness

Integrity

Charisma

Adaptability
Making the system flexible and dynamic
enough to adapt to the user’s explicit or
implicit preferences and feedback.

Supervising network performance

Showing a system is open to
influence from the user is a big
building block of trust.

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Figure 7: Controllability, enabling users to take a participatory role in the decision-making process

Figure 8: Adaptability, showing the user that their preferences have influence

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Integrity

Do you share my values?

The concept of integrity in an AI systemcomes down to whether the user feelsthat the system is honest, and whether it
adheres to the same high ethical standards
as the user.
There are two ways UX design in AI
contributes to the impression of integrityin a system:
Veracity of promises
Setting the right expectations for the user
by clearly communicating the capabilities
and limitations of the AI system – knowing
what it can promise to do and follow
through on and what it cannot or is
not designed to do.

Transparency on safety,
security and permissions
Making sure the user understands whatkind of data is collected, how it is collected,
for what reason and how it will be used.

Figure 9: Components of trust – integrity

Competence

Benevolenceand openness

Integrity

Charisma

Figure 10: Setting the right expectations and showing the user the different possible outcomes of the AI’s recommendations

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Charisma

Do I like you?

And finally – charisma. Charisma in a
AI system comes down to crafting it in a
way that gives it general charm and appeal,
and that the system looks and sounds
appropriate to the task it is handling.
UX designers and practitioners cancontribute to the attractiveness of an
AI system by implementing:Figure 12: Appropriate tone of voice for the scenario

Visual appeal
Crafting the system’s look and feel inan aesthetically pleasing and visuallyorganized way, so that the humanuser perceives it to be more efficientand understandable.
Tone-of-voice suitability
Making sure that the style and tone of
the copywriting and voice interactions
are aligned with the message that you
want to convey, the desired personalityof the system, and the traits of the targeteduser group.
Figure 11: Components of trust – charisma

Competence

Benevolenceand openness

Integrity

Charisma

When rejecting an action, the system behaves like a junior engineer and asks about thereason for the rejection, so it can learn from the user’s experience

Figure 13: Clear and organized visual layout contributeto increased perceived trustworthiness12

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Beyond the building blocks

Other factors that can affect trust in AI

All the elements that have been mentioned
so far in this framework are those thatare represented in one way or another in
the interaction and the interface of the AI
system, and are therefore relevant to the
UX design.
There are numerous other factorsthat can affect trust in an AI system, andalthough these additional factors cannot
be translated into the interface as UX, they
are nevertheless essential elements in thebuilding of a trust-based relationship.
For example:
Accuracy
If the output results, predictions,recommendations and decisions of
the system are not accurate to begin
with, then the system will not meet the
aforementioned criteria needed to satisfy
the “competence” component.
Bias in AI
One widely discussed topic in the AI
community is the concept of bias –
specifically that ML selects incomplete,uninclusive or biased data sets to train themodel, whether deliberately or otherwise.
This will result in output that is not fairand biased towards a group of users –meaning that the core pillar of “integrity”
will not be met.
Laws and ethics
Without sufficient and clear laws anda code of ethics that regulates therelationship between the user and theAI system, for example defining who is
responsible if the output of the systemaffects the user in a negative way, then thetrust in the human-AI relationship will notsurvive any potential mistakes thesystem makes.
With the right design principles inplace, AI-powered OSS could open
up a more powerful future
for networks.

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At the controls of the entire network

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Conclusion

The future of human-tool relationships

When introducing AI-powered software
like our Cognitive OSS services, the notionsof what a network is, what the owner or
network operator’s role is and what asystem provider contributes are changing.
The interaction will be on a muchhigher and more abstract level. Insteadof changing gears in the car, the focus will
be on the passenger’s journey. Insteadof having field technicians manuallyclimbing towers to fine-tune the radio,
business operators will collaborate with
the AI machine to reach the organization’s
business intent, impacting the roles of thesystem providers and the network operator.
A user-centric design process will beeven more important when designing
AI-powered services than in traditional
services. If users and organizations aregoing to trust the AI-powered system, forexample an airplane without a pilot, thetrust must be designed into the system andthe relationship from the very beginning.
Without it, these services will fail.
Designing an OSS AI solution thattakes critical decisions that can affect theperformance of an entire network is about
more than focusing on building better AI
models and algorithms. Trust will be themost vital factor in human-AI interaction.
If we want the users of our AI solutions to
accept handing over more critical tasks anddecisions to AI, we need to design them tobe trustworthy.

Designing for trust is a cornerstoneof building successful AI systems.

The essential human in the loop

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Authors and references

Mikael Eriksson Björling
Mikael is Experience Design Line Manager at Ericsson Experience Design Lab and an Ericsson Evangelist. He waspreviously Director at the Networked Society Lab. His specialty is in understanding how new behavior, emerging
technologies and new industry logics are shaping the future society and in the intersection of these areas design
great user and customer experiences. Mikael believes that with the ongoing digital transformation we have a great
opportunity to shape a better world. Mikael joined Ericsson in 1998.

Ahmed H. Ali
Ahmed is visual and user experience designer at Ericsson Experience Design Lab. With over 15 years of experience,
his career inside and outside Ericsson was focused on designing digital systems that satisfy users’ needs and help
them achieve their goals by bringing design thinking to the product development process, applying human-computerinteraction best practices, and delivering UI/UX concepts and insights. Ahmed joined Ericsson in 2018 and he holds
an M.A. in visual design from the University of Hertfordshire in the UK.

• Andras, P., Esterle, L., Guckert, M., Han,
T. A., Lewis, P. R., Milanovic, K., ... &Urquhart, N. (2018). Trusting intelligent
machines: Deepening trust withinsocio-technical systems. IEEE
Technology and Society Magazine,
37(4), 76-83.
• Bathaee, Y. (2017). The artificial
intelligence black box and the failureof intent and causation. Harv.
JL & Tech., 31, 889.
• Commerce is a conversation:
Survey on Amazon Echo and Voice
Assistants – Experian Insights, 2016.
• Gobble, M. M. (2019). The Road to
Artificial General Intelligence.
• Goertzel, B. (2007). Artificial general
intelligence (Vol. 2). C. Pennachin (Ed.).
New York: Springer.

• Intelligent Economies: AI’stransformation of industries and society– The Economist Intelligence Unit, andMicrosoft, 2018.
• Minsky, M. (1982). Semantic
information processing.
• Muir, B. M. (1987). Trust between
humans and machines, and the designof decision aids. International journal ofman-machine studies, 27(5-6), 527-539.
• Noessel, C. (2017). Designing agentive
technology: AI that works for people.
Rosenfeld Media.
• Ransbotham, S. et all. Artificial
Intelligence In Business Gets Real –Pioneering Companies Aim for AI at
Scale. MIT Solan, 2018.

• Sanders, K., Schyns, B., Dietz, G., & Den
Hartog, D. N. (2006). Measuring trust
inside organisations. Personnel review.
• Schoorman, F. D., Mayer, R. C., & Davis,
J. H. (2007). An integrative model oforganizational trust: Past, present,
and future.
• Siau, K., & Wang, W. (2018). Building
trust in artificial intelligence, machinelearning, and robotics. Cutter BusinessTechnology Journal, 31(2), 47-53.
• Stern, M. J., & Coleman, K. J. (2015).
The multidimensionality of trust:Applications in collaborative natural
resource management. Society &Natural Resources, 28(2), 117-132.

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