Ericsson White Paper
GFTL-23:000757 Uen
June 2023

Trustworthy AI- What it means
for telecom

Trustworthy AI - What it means for telecom
Content
June 2023

2

Content
Introduction						3Human Agency and Oversight				4Transparency						5Privacy and Data Governance				8

Diversity, Non-discrimination and Fairness		

9Technical Robustness and Safety				11Social and Environmental Well Being			

13

Conclusion						15Glossary						16References						18Authors							19

Trustworthy AI - What it means for telecom
Introduction
June 2023

3

Introduction
Billions of people have come to trust and depend on modern telecom systems to support
their needs and quality of life. As these systems adopt new technologies, it’s important trustis maintained by understanding and addressing any new risks. Artificial intelligence (AI)differs from traditional software in its construction and operation and may introduce newand varied risks, calling for new countermeasures and guardrails. For example, the large
amount of data used for AI training raises the possibility of privacy risks. Development
procedures must ensure that AI models learn what is intended. The model operation
should be thoroughly understood, for example, using explainability techniques. In short, tomaintain the trustworthiness of the overall system, AI must itself be trustworthy: meaning,it should operate as intended and do no harm physically or ethically.
Governments, companies, and standards bodies around the world are taking notice of thesefacts and creating requirements regarding the trustworthiness of AI systems. The upcoming
European Union AI Act [1] is one such effort. It follows principles written by the European
Commission High-Level Expert Group in their “Ethics Guidelines for Trustworthy AI” [2].
Ericsson has adopted these guidelines. The framework breaks trustworthiness into sevenspecific areas. This paper explores how six of these areas apply to AI in telecom systems,
some of which are depicted in Figure 1.

Transparency• Explainable RL• Explainable ML /MR• Explainable GNN• XAI Quantiﬁcation• Causal AI

Privacy & Data Governance• ML for Security• Security for ML• Privacy Preserving AI• Federated Learning

Figure 1: Ericsson's activities and technologies

Technical Robustness& Safety• Safe RL• Automated model
quality assurance• Invariance & directional
expectation tests• NFL/RL for safety• Formal veriﬁcation

Societal Environmental
wellbeing• Energy management
use case• AI for Good

Trustworthy AI - What it means for telecom
Human Agency and OversightJune 2023

Human Agency
and Oversight
Human agency and oversight requirements make sure that humans can always intervene in
AI-controlled systems before things like fundamental rights and safety have the potentialof being affected—in other words before they could pose any harm. Implementation of suchrequirements means having “humans in the loop”, the difficulty of which depends on thetimescale of the decision and the criticality of the system.
Sometimes AI operates at timescales, which are much too fast for human intervention, suchas the optimization of radio operations in a base station. In telecom, these uses typicallydo not have a direct impact on the rights and safety of individuals. However, they candramatically impact network operations, which can subsequently affect humans, so theyneed careful assessment.
Implementing human agency and oversight in AI-aided network operations requires user
research, product function design, and user testing to make sure that network operationengineers can detect and intervene when needed. The human-machine interaction design
must be aligned with existing network operation processes, rely on existing interfaces, andprovide actionable information to users.
The interface for human agency and oversight may vary depending on the use case andusers. It can be a GUI, CLI, Rest-based API, or even a physical interface (for example, alight). For instance, an AI system that detects and predicts network-wide congestion may
use a graphical interface embedded in a dashboard used for regular network design andoperations. The system may also send alerts if AI performance deteriorates (for example, byreporting too many false network congestions) to the network operations center (NOC). The
alerts should include everything the NOC engineer needs, including reasons for the problem,
potential root causes, and solution suggestions. The engineer then has three choices: toswitch to a non-AI based function; to understand and solve the AI issue; or to escalate it.
Alert volume should be considered, to not overwhelm a potentially already heavily loaded
NOC. Explainable AI methods can help in generating needed, and user-tailored, reasons forproblem.
The use case, user knowledge, and persistence of the AI notification influence the actions to
be taken. Intermittent or ephemeral events may need to be repeated. More serious events
may need an escalation path.

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Trustworthy AI - What it means for telecom
Transparency
June 2023

Transparency
Trust can arise from understanding how a system works, or from experience using it overtime. The complexity and black-box nature of AI can lead to suspicion, particularly whenpeople feel that the AI’s own creators don’t fully understand how it makes decisions, andwhat exactly it has learned. Greater transparency can help build trust by understanding andexplaining AI models to humans. Explainable AI (XAI) refers to methods and techniquesthat produce models which show why and how an AI algorithm has made a certain
decision. It helps stakeholders understand how decisions are being made in different
formats: by identifying what input factors were most important in making an inference andby providing explanations and responding to “why” and/or “what-if” questions. It also helps
a human operator in decision-making. If the operator is not satisfied with a response, afurther investigation can be performed, using computational argumentation techniques.
Creators of AI for telecom should provide XAI methods to help build the trust of theirdirect customers (for example, service providers), and in turn, enable them to build trust
for their subscribers. The explainability of AI should start with design and continue
through implementation, as a built-in feature, to ensure transparency throughout the AI
development lifecycle. In addition, different XAI techniques should be researched, anddeveloped to explain different types of machine learning (ML) methods.
The Ericsson white paper [3] presents different XAI techniques applied to different AI/
ML methods, including machine reasoning (MR), and reinforcement learning (RL). The
explanations generated by these XAI techniques not only help explain the decisions tohumans but also support automation, for example, in root cause analysis when combinedwith other AI techniques.
Explainability of ML, that is, feature analysis techniques (including SHAP and LIME) canbe used in multiple telecom use cases to identify and explain the problems and root causesof specific ML model outputs in addition to ensuring the overall correctness of the MLmodels. These techniques can be applied to ML-based predictions to investigate the mostimportant features that contribute to certain prediction results and validate the correctnessof the ML model. The results of these explainability techniques can support MR components
in identifying the root cause of the problem. 5G slice assurance is one such use case wherethese techniques are thoroughly investigated and tested. In this use case, certain Qualityof-Service (QoS) requirements (such as throughput, latency, and availability) are agreed
upon with the customer in a service level agreement (SLA) and must be met throughout

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Transparency
June 2023

the lifecycle of the slice. ML models are used to proactively identify any potential violationof the agreed QoS requirements. Upon a violation in prediction, explainability techniques
are applied to identify the most contributing features which in turn helps NOC Engineersin identifying the root cause of the problem [4]. These techniques can be applied tomultiple use cases in a similar manner, like cell shaping and key performance indicator(KPI) degradation prediction, focusing on latency- and network throughput–related
optimizations.
Explainability of RL: RL is suitable to solve many cellular network problems due to its
dynamic nature, online training, interaction with the environment, and outstanding
performance over traditional rule-based techniques for the telecom domain. An RL agent
performs an action (such as applying a policy) in an environment to maximize rewards. The
explainability of RL includes methods applied to different RL components, such as rewards
and policy explanations.
In a base station, the antennas are tilted up, down, or kept the same to optimize KPIs, thatis, the coverage of the network, increased quality by reducing interference, and capacity/
throughput of the network. Coverage refers to the area from which a UE can access thecellular network, while capacity refers to the amount of traffic the cellular network canhandle simultaneously. Remote electrical tilt (RET) refers to adjusting the tilt of the antenna
by an RL agent to optimize the above mentioned KPIs. Increasing the down-tilt reduces
the area covered by the antenna, with the risk of leaving a certain area without coverage
but increases the capacity in the covered area due to a stronger signal. In contrast, up-tilt
results in a larger area covered but lower capacity due to a weaker signal. Explainability
is important in the RET optimization of antennas in a cellular network. Explanations help
in understanding the reasons behind a specific adjustment. The following explainablereinforcement learning (XRL) methods are applied to this use case [5]:Total Reward

Multiple
sub-rewards

Figure 2: Total Reward is decomposed into multiple sub-reward functions for better explainability

6Trustworthy AI - What it means for telecom
Transparency
June 2023

7

RSRP

drDQN

DQN
State

State
Q-values

SINR

Q-values

Throughput

Figure 3: Reward decomposed into coverage (RSRP), quality (SINR),
and capacity (throughput) sub-functions for RET use case

• Reward decomposition provides intuitive contrastive local explanations for the agent’s
decisions by decomposing the reward into multiple sub-functions to adjust the tilt (seeFigures 2 and 3), while achieving the same performance as the original DQN algorithm.
The generated contrastive explanations are very user-interpretable, as they concisely
answer questions in the form of “why did you decide to down-tilt instead of up-tilting?”• The Linear Model U-Tree (LMUT) reaches high performance while employing a fullytransparent linear model capable of generating both local and global explanations (seeFigure 4), however, it is less transparent than reward decomposition.
• Autonomous Policy Explanation summarizes the trained policy and explains it in natural
language, thus enabling the policy to be understood by everyone including non-experts.
• Contrastive explanation through embedded self-prediction produces a local explanation
about the internal representation of the RL agents (intermediate or inner layers of the
deep neural network). It compares two different actions, such as why the antenna is tilted
down and not up.
RSRP
≤0.34

RSRP

Q-values
SINR

RSRP≤-0.72

Q =22.78 -3.77*RSRP 8.47*SINR +6.43*Tilt

Tput

Tilt

Q =25.79 -2.08*RSRP– 2.63*SINR -3.77*Tilt

Q =22.3 -6.0*RSRP 0.38*SINR -9.17*Tilt

Figure 4: LMUT distils a transparent model from a trained agent by transferring the NN to adecision tree with linear regression in the leaf node, where the tree is inherently transparent

In addition to enabling transparency and AI automation, Ericsson has seen the potentialof reducing the input feature set by using XRL. A novel method was developed forconnecting explanations from both the input (feature analysis) and output (reward) ends
of a black-box RL model, resulting in fine-grained explanations [6]. Reward prioritization,
performed by the user, generates two different levels of explanation, and allows RL agent
reconfigurations when unwanted behaviors are observed.

Trustworthy AI - What it means for telecom
Privacy and Data Governance
June 2023

Privacy and Data
Governance
It is usually necessary or desirable to prevent AI systems’ data from being disclosed. If
the data includes the personal data of individuals, it may be subject to stringent legal
requirements. Business data might contain intellectual property or be subject to contractual
constraints. Sometimes, the data or other information related to it can be inferred froman AI model, especially when combined with publicly available data sources. Laws andregulations requiring privacy for individuals, such as the EU General Data Protection
Regulation (GDPR) [13], predate the mass adoption of AI across industries. Despite being
written in a manner to make them future-proof, such regulations don’t necessarily anticipate
the extent of potential AI risks. There are also often contractual requirements for privacy.
And even in the absence of such requirements, it is generally understood that maintaining
privacy is ethically the right thing to do.
Ensuring privacy in telecom AI impacts the entire AI lifecycle and requires the application ofPrivacy by Design and Privacy by Default (as defined by GDPR [7] and other global privacylaws), starting with controls where the training data is collected and continuing throughmodel use (inference). Many controls relevant to AI are the same as or similar to those usedin other types of data processing. The nature of the data, the purpose of the collection,
who will use it, and how and when it will be used, should be clearly communicated. Only
the minimum amount of data required for the intended purpose should be collected.
Unnecessary fields should be redacted or masked. Controls like pseudonymization,
encryption, authentication, and authorization should be used to ensure appropriate access.
Uses of the data should be logged and auditable. Once no longer needed, data should besecurely erased.
Since AI models learn from the training data, in some sense the data is encompassed
within the model. Therefore, it’s unsurprising that attacks exist on models to extract thetraining data itself or make inferences about it. Privacy enhancing technologies (PETs),
including differential privacy, exist to help make AI models less susceptible to such attacks.

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Privacy and Data Governance
June 2023

Developers can also analyze model sensitivity to data extraction, for example, how many
queries are required. Such metrics can inform decisions about the privacy risk involved in
deploying the model.
Data can be exposed during training if it is improperly secured. When federated learning isused, multiple participants with independent datasets can contribute to building a single
global model. This helps them keep their datasets private, but the protocols used must be
carefully designed to ensure no information is leaked.

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Diversity, Non-discrimination and Fairness
June 2023

Diversity, Nondiscrimination
and Fairness
Bias in models can come from bias in the training data, which itself may stem from historicalprejudices and inequities. It may also be caused by disproportional representation. One
example is natural language processing systems used for interaction with subscribers,
such as chatbots, or support ticketing systems. Even within a single language, training
such systems should consider different speaking styles, idioms, and education levels. Not
everyone speaks perfectly, but everyone deserves the same level of service.
Steps to avoid bias include:• understanding what categories exist in the input data that need to be treated equally• making sure each category is adequately represented in the training set, regardless of size
• being aware of historical inequities that might be relevant to the problem at hand andadjusting the data accordingly• ensuring model robustness, including thorough testing• considering categories separately throughout the model development process, so that themodel performs well for each
Even a seemingly solely technical problem can have a bias that impacts people, sometimes
in subtle ways. Consider the use of AI to structure and operate mobile networks. This canbe impacted (or biased) based on how and where that data is collected, or by cognitive bias
already present in the system’s creators. If data collection is skewed toward a particulargroup (for example, people with a certain economic status, which might correlate to otherfactors such as race), the resulting system might be inadvertently biased. Telecom systems
have to work everywhere in the world and should provide equally good service to all people.

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Diversity, Non-discrimination and Fairness
June 2023

If more investment (that is, equipment, optimization, effort) goes into certain areas, someof these areas might get disproportionately better service. Such areas could have different
usage patterns. When ML that is used to plan, deploy, optimize, and operate networks istrained on data collected in some regions, networks (or products) might be created thatwork well only in those regions.

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Technical Robustness and Safety
June 2023

Technical
Robustness andSafetyHuman safety is not typically directly impacted by telecom operations but can be affectedby situations like loss of service. Telecom systems can be important components inemergency communications and disaster handling. Another example is an application likeautonomous vehicles, where the loss of communication might impact the ability to proceedsafely.
AI can assist in these situations when it makes the network itself more robust. But thismeans that the AI itself must be robust. Careful attention to AI quality must be taken during
training and deployment. Fallback mechanisms should be in place for cases where the AI
cannot decide or makes an out-of-bounds decision to, for example, transfer the control to ahuman operator.
AI is also subject to new types of attacks. Data extraction attacks, which have already been
mentioned, compromise the training data and potential privacy. Researchers have also
demonstrated attacks on inference using adversarial examples. Carefully crafted inputs are
fed to the model, causing its inference to be biased in a direction chosen by the attacker.
Where the attacker has access, poisoning the training data can be used to influence model
operation. A motivated attacker might use these mechanisms to affect network operations
or steal a service. The training pipeline, resulting models, and surrounding application
context should be analyzed for susceptibility to such attacks.
Automated model quality assurance is crucial for technical robustness and safety. Models
must be thoroughly tested against performance metrics that reflect potential real-world
scenarios. These metrics are use case and model specific. For instance, in the case of aclassification model, accuracy, false positive or false negative rates might be measured,

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Trustworthy AI - What it means for telecom
Technical Robustness and Safety
June 2023

while in the case of clustering, Silhouette Coefficient [8] and Dunn Index [9] might bechosen.
It is also important to communicate this information in an understandable and usefulmanner to model recipients, keeping in mind that they may not have expertise in AI. For
example, when communicating a performance metric about a model, information should be
included about its meaning and what values are considered good or bad.
Since a model’s performance depends on the data set used for training and evaluation,
data quality is essential for model quality. A training data set should accurately represent
reality and cover the events or objects of interest: the training data set must have thesame statistical properties the real objects have, and if there are relationships between theattributes of the real object or among the real objects, those relationships must be preservedin the training data set.
The RET use case mentioned earlier shows that poor AI decisions could lead to interference
and compromised network performance. Anywhere an AI algorithm is used in the operationof a network, a failure of that algorithm can lead to inefficiency, instability, or, at worst,
downtime.
Another important concern is that AI models could, inadvertently or maliciously, takeactions that are unsafe for humans. As already mentioned, telecom systems by nature are
not safety-critical, and the danger could be reduced network performance. One scenario
could be when the model explores the space of all possible states and actions. This becomessignificantly important for RL, where space exploration is seen as an effective way to train
an RL agent to capture a near-optimal policy. However, unchecked exploration can lead thesystem to visit a dangerous state, for example, when the system tries to tilt the antenna at
an overly high angle. Safe RL methods provide a shield to block unsafe actions that might
result from free exploration of state-action spaces. The intention is to allow the agent somestate exploration of the environment while having boundaries using safety specifications
defined by a human developer. The specifications, or boundaries, can be user dependent.
One such use case where these techniques are successfully tested is RET Optimization [10].
Conducting invariance and directional expectation tests is also essential to assess andassure the model’s robustness. In an invariance test, label-preserving perturbations areapplied to inputs and the model prediction is expected to remain the same. In a directional
expectation test, a set of perturbations are made to the input which should have a
predictable effect on model output.

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Trustworthy AI - What it means for telecom
Social and Environmental well being
June 2023

Social andEnvironmental
well being
AI can be used to create positive benefits for society, such as by helping protect theenvironment. Ericsson considers communication to be a fundamental human right, so theavailability of the network is core to societal well-being. The findings of this white paper
support this conclusion. AI, by helping service providers create telecom networks thatare more reliable, ubiquitous, and inexpensive, contributes to the social goal of universal
communication. But there are possible negatives, such as the privacy aspects discussed
above. ML training can be energy intensive, so careful cost-benefit analysis is needed before
it is employed. ML might be used where there are potential safety or societal risks (forexample, control of critical infrastructure). Minimizing those risks is important, and this canbe done using the techniques described in this white paper.
AI can help improve network operations and energy consumption. Large volumes ofdata can be used to optimize important goals like performance, reliability, capacity, andenergy usage. Traditional optimizations, written by programmers, typically used only afew parameters and simple algorithms with limited results. AI allows large numbers ofparameters to be used, better optimizations, and therefore better performance. Ericssonteams looking at specific use cases have shown that AI can lead to significant energy
savings compared to traditional algorithms. [11].
AI in telecom can be useful beyond the simply better operation of the network. During
the COVID-19 pandemic, Ericsson engaged in a joint project with a service provider,government officials, and two hospitals [12]. The service provider provided anonymized andaggregated data about people’s movements, taken from their network. This was combinedwith vaccination, antibody test, and hospital COVID patient admission data. A series of14

Trustworthy AI - What it means for telecom
Social and Environmental well being
June 2023

ML models used the data to predict admissions two to three weeks into the future. In
eleven of sixteen weeks, the predictions had an error rate of less than thirty percent. Better
resource planning for hospitals, especially during a crisis, leads to better patient care. This
demonstrates that AI and telecom networks and data can be used to benefit society in noveland perhaps unexpected ways.

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Trustworthy AI - What it means for telecom
Conclusion
June 2023

Conclusion
The benefits of AI in telecom networks are only just beginning to be leveraged but willclearly be an important and integral element in future networks. Trusting those networks
requires trusting the AI, which can be achieved by following the presented guidelines.
However, these present a number of challenges:• Ensuring that humans retain oversight and control over AI systems, even when highly
automated and operating at high speeds.
• Providing information about AI operations, using techniques such as XAI, while
maintaining a fine balance between the privacy of models and data and transparency• Protecting the data of users and businesses, while still using it to deliver AI that benefits
them• Understanding how AI might affect different communities and preventing adverse
impacts• Making AI systems robust and safe, yet practical to train and deploy.
• Considering how AI affects society broadly, both to prevent adverse impacts and topromote beneficial uses.
Trusting the AI, that is having confidence that it operates as intended and does noharm, requires diligence in addressing each of these challenges. Ericsson is working tocontinuously improve its AI systems, making them, and consequently their products, moretrustworthy.

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Trustworthy AI - What it means for telecom
Glossary
June 2023

Glossary
AI

Artificial Intelligence

CLI

Command Line Interface

DQN

Deep Q Network

GDPR General Data Protection Regulation, https://gdpr-info.eu/
GUI

Graphical User Interface

KPI

Key Performance Indicator

LIME

Loacl Interpretable Model-agnostic Explanations

LMUT Linear Model U-Tree
ML

Machine LearningMR

Machine ReasoningNN

Neural Network

NOC

Network Operations Center

PET

Privacy Enhancing Technologies

RET

Remote Electrical Tilt

RL

Reinforcement Learning

RSRP

Reference Signal Received Power

SHAP

SHapley Additive exPlanations

SINR

Signal to Interface plus Noise Ratio

SLA

Service Level Agreement

UE

User Equipment

XAI

Explainable Artificial Intelligence

XRL

Explainable Reinforcement Learning

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References
June 2023

References
1. https://artificialintelligenceact.eu/
2. https://www.aepd.es/sites/default/files/2019-12/ai-ethics-guidelines.pdf
3. Rafia Inam, Ahmad Terra, Anusha Mujumdar, Elena Fersman, Aneta Vulgarakis,
Explainable AI - How humans can trust AI. EricssonWhite Paper, April 2021
4. A. Terra, R. Inam, S. Baskaran, P. Batista, I. Burdick and E. Fersman, “Explainablity
Methods for Identifying Root-Cause of SLA Violation Prediction in 5G Network,” in IEEE
Global Communications Conference, 2020
5. Franco Ruggeri, Ahmad Terra, Rafia Inam, Karl-Henerik Johansson, “Evaluation ofIntrinsic Explainable Reinforcement Learning in Remote Electrical Tilt Optimization”, in8th International Congress on Information and Communication Technology, 2023
6. Ahmad Terra, Rafia Inam, Elena Fersman. BEERL: Both Ends Explanations for
Reinforcement Learning. Journal of Applied Sciences, Special issue “Explainable
Artificial Intelligence”, Vol 12, No. 21, November 2022
7. EU GDPR Privacy by Design, https://gdpr-info.eu/issues/privacy-by-design/
8. Peter J. Rousseeuw (1987). "Silhouettes: a Graphical Aid to the Interpretation andValidation of Cluster Analysis". Computational and Applied Mathematics. 20: 53–65.
doi:10.1016/0377-0427(87)90125-7.
9. Dunn, J. C. (1973-09-17). "A Fuzzy Relative of the ISODATA Process and Its Use in
Detecting Compact Well-Separated Clusters". Journal of Cybernetics. 3 (3): 32–57.
doi:10.1080/01969727308546046. S2CID 120919314.
10. A. Nikou, A. Mujumdar, V. Sundararajan, M. Orlic and A. V. Feljan, "Safe RAN control: A
Symbolic Reinforcement Learning Approach," 2022 IEEE 17th International Conference
on Control & Automation (ICCA), 2022
11. https://www.ericsson.com/en/news/2023/4/ericssons-service-continuity-ai-appdelivers-25-percent-energy-savings-for-far-eastone
12. Taghia, J., Kulyk, V., Ickin, S. et al. Development of forecast models for COVID-19
hospital admissions using anonymized and aggregated mobile network data. Sci Rep
12, 17726 (2022). https://doi.org/10.1038/s41598-022-22350-6
13. EU General Data Protection Regulation, https://gdpr-info.eu/t18

Trustworthy AI - What it means for telecom
Authors
June 2023

Authors

Jim Reno is a Distinguished Engineer at Ericsson, where he works on security aspects ofArtificial Intelligence as applied to telecommunication systems. He has more than 40 years
of industry experience in fields including system software (operating systems, networking,system management, and cloud native systems), payment system security, authentication,authorization and identity management.

Rafia Inam is a senior research manager at Ericsson Research and Adjunct Professor at KTH
in research area Trustworthy Artificial Intelligence. She has conducted research for Ericsson
for the past nine years on 5G for industries, network slices and network management; AI for
automation and intelligent transport systems. She specializes in automation and safety forcyber-physical systems and collaborative robots, trustworthy AI, and explainable AI. She
won Ericsson Top Performance Competition 2021 on her work on AI for 5G network slice
assurance, and was awarded Ericsson Key Impact Award 2020, and Key contributor award
2020. Rafia received her PhD in predictable real-time embedded software from Mälardalen
University in 2014. She has co-authored 55+ refereed scientific publications and 55+
patent families and is a program committee member, referee, and guest editor for several
international conferences and journals.

19Trustworthy AI - What it means for telecom
Authors
June 2023

Attila Ulbert joined Ericsson in 2015 and he is currently Artificial Intelligence System
Manager. In his enthusiastic journey with Ericsson, he lead the development of Ericsson’s
AI platform, and worked on fundamental AI studies on security, trustworthiness, andindustrialization. Attila has a PhD in Informatics from Eötvös Loránd University. He is amarathoner.

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