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Abstract

AI plays a key role in current cyberspace and future immersive ecosystems that pinpoint user experiences. Thus, the trustworthiness of such AI systems is vital as failures in these systems can cause serious user harm. Although there are related works on exploring trustworthy AI (TAI) metrics in the current cyberspace, ecosystems towards user-centered services, such as the metaverse, are much more complicated in terms of system performance and user experience assessment, thus posing challenges for the applicability of existing approaches. Thus, we give an overlook on fairness, privacy and robustness, across the historical path from existing approaches. Eventually, we propose a research agenda towards systematic yet user-centered TAI in immersive ecosystems.
Towards User-Centered Metrics for Trustworthy AI in Immersive Cyberspace
Pengyuan Zhou1,Benjamin Finley2,Lik-Hang Lee3,Yong Liao1,Haiyong Xie1,Pan Hui2,4,
1University of Science and Technology of China, 2University of Helsinki, 3Korea Advanced Institute of
Science and Technology, 4The Hong Kong University of Science and Technology
Abstract
AI plays a key role in current cyberspace and fu-
ture immersive ecosystems that pinpoint user ex-
periences. Thus, the trustworthiness of such AI
systems is vital as failures in these systems can
cause serious user harm. Although there are related
works on exploring trustworthy AI (TAI) metrics in
the current cyberspace, ecosystems towards user-
centered services, such as the metaverse, are much
more complicated in terms of system performance
and user experience assessment, thus posing chal-
lenges for the applicability of existing approaches.
Thus, we give an overlook on fairness, privacy and
robustness, across the historical path from exist-
ing approaches. Eventually, we propose a research
agenda towards systematic yet user-centered TAI in
immersive ecosystems.
1 Introduction
Today, artificial intelligence (AI) methods have shown state
of the art performance in many fields and are becoming in-
creasingly widespread in many areas of everyday life, in-
cluding recommender systems [Cheng et al., 2016], health
care [Norgeot et al., 2019], smart factory [Shiue et al., 2018],
financial modeling [Lin et al., 2011], marketing [Cui et
al., 2006], education, science, and commerce [Jordan and
Mitchell, 2015]. However, such integration also allows AI
systems to access large datasets from countless users. AI sys-
tems can leverage these datasets along with significant net-
working and computing power to learn very granular and po-
tentially sensitive user behavior. Additionally, to most users,
AI systems appear as black-boxes that provide little insight
into their internal decision-making process. This arouses
moral concerns surrounding AI systems and especially the
trustworthiness of AI for the sake of fairness, privacy, secu-
rity and system reliability [Adadi and Berrada, 2018].
To address these concerns and strengthen human trust in AI
systems, the area of Trustworthy AI (TAI) has recently seen
significant attention from government entities, such as the Eu-
ropean Commission [HLEGAI, 2019], United States Depart-
ment of Defense [Heaven, 2021], China’s Ministry of Science
The Corresponding Author: Pengyuan Zhou, E-mail:
(pyzhou@ustc.edu.cn)
and Technology, and numerous technology giants such as
IBM, Google, Facebook. The major goal of TAI is to ensure
the protection of people’s fundamental rights while still al-
lowing responsible competitiveness of businesses [European
Union, 2012]. The term TAI has been around for years and
was boosted by the well-known EU guidelines on TAI pub-
lished in 2019 [HLEGAI, 2019]. The concept grew rapidly
as the number of research papers in Google Scholar with the
term in the title or abstract increased from 6 to 1,040 over
2017 – 2021.
In the TAI domain, TAI metrics are naturally a major is-
sue and critical to accurately measuring the degree of sys-
tem trustworthiness and the amount of protection offered by
AI-enabled technologies. As a quantification of trustworthi-
ness, a TAI metric can be a form of one or multiple system
properties or system states to assess trustworthiness. The ap-
propriate TAI metrics can vary in different domains due to
various scenarios, user demand, processed data, adversaries,
regulations, and laws. As integration of several related re-
quirements, TAI should contain multiple ‘dimensions’, even
though multi-criteria evaluation will lead to increased com-
plexity. Despite the large number of case-by-case metrics
used in current literature, a comprehensive and systematic
outline of TAI focusing on metric selection has yet to be
proposed, resulting in challenges for metric choices for non-
experts and even professionals. Furthermore, future immer-
sive ecosystems, such as the metaverse, are going to incorpo-
rate more complex systems that blend the virtual and physi-
cal worlds, and, more importantly, complicated definitions of
system performances and user experiences. Therefore, cur-
rent metrics and metric selection logic might need enhance-
ments and evolution to fit the complexity.
Related Surveys and Our Scope. We acknowledge a num-
ber of existing surveys on ethical guidelines for AI [Smuha,
2019], big data [Mantelero, 2018]and robotics [Torresen,
2018]; along with surveys on specific domains (e.g., fi-
nance [Lin et al., 2011]) and types of AI applications (e.g.,
recommender systems [Gunes et al., 2014]). In contrast, this
survey explores recent trends and advancements in TAI met-
ric selection across two important domains, and sheds light on
the metric selection logic for future user-centered cyberspace.
Note that legal compliance (lawful AI) is beyond the scope of
this survey. Specifically, we focus on the technical metrics for
fairness,privacy, and, robustness. Other TAI requirements,
including transparency and accountability, do not have suffi-
cient literature yet for survey purposes and thus are left for
future work. Since AI is a socio-technical system instead of
a mathematical abstraction, this work requires readers to per-
sonalize the metric selection on demand.
Review Methodology. The selection of publications was
conducted in four steps. First, we search in Google Scholar
for “Trustworthy AI”, and usecase names (e.g., recommen-
dation system) plus TAI requirements (e.g., fairness) and find
that relevant publications are spread across multiple scientific
journals and conferences. Second, we choose papers primar-
ily from three scientific repositories: ACM Digital Library,
IEEE Explore, and Springer. Third, we try to select the top
cited or top-venue (e.g., KDD and WWW) papers if proper
papers exist. Finally, if no proper references are found from
the second and third steps, we choose the most feasible refer-
ences by the searching through Google Scholar with related
keywords such as ”AI in Networking” and filter by topics
with relevant to TAI.
Contributions. This survey serves as a first effort to out-
line the critical TAI metrics in current domains, capture the
general logic of metric selection, and call for advancing user-
centered metrics for immersive ecosystems and autonomous
metric selections. Specifically, our contributions are three-
fold. First, we describe the importance of the TAI require-
ments this article focuses on (Section 2). Second, we out-
line two most fundamental domains and dive into specific
use cases therein to summarize TAI metrics in the current
cyberspace (Section 3). Finally, we summarize the lessons
learned from existing TAI metric/ selection methods, and dis-
cuss the research agenda for future ecosystems’ with TAI as
well as the related metric selection methodology (Section 4).
2 TAI: User-centered Requirements
As mentioned, we focus on fairness,privacy,robustness,
and the key metrics to meet these requirements in different
domains. In this section, we present an overview of these
requirements and list some common metrics.
Fairness. AI, especially machine learning, often presents
statistical discrimination due to non-identical data distribu-
tion or resources, which leaves certain privileged groups with
performance advantages and others with disadvantages. The
learning bias, regardless if generated on purpose or acciden-
tally, exacerbates existing resource inequity and further harms
fairness in society [Paluck et al., 2019]. Therefore, the AI
community has been putting efforts on measuring the fairness
of AI systems to mitigate algorithmic bias.
Privacy. Privacy is fundamental yet hard to define ex-
plicitly. Nissenbaum [Nissenbaum, 2004]defines privacy
in terms of contextual integrity and contextual information
norms dictating how information may be used or shared. As
agreed by most researchers, privacy is a multi-dimensional
concept [Laufer, 1973]and thus is normally assessed via mul-
tiple metrics focusing on the exposure of private information.
Robustness. Due to natural variability or dynamic system
conditions over time, predicting how future conditions will
change might be hard or impossible. The term deep uncer-
tainty, in this scenario, is more proper to describe the issue in
AI. Deep uncertainty is defined as a situation in which par-
ties to a decision do not know or cannot agree on how the
system (or part of it) functions, the importance of the vari-
ous outcomes of interest, and/or what the relevant exogenous
inputs to the system are and how they might change in the
future [Maier et al., 2016]. Under deep uncertainty, typi-
cally, multiple environmental state factors, i.e., future con-
ditions, jointly affect the decisions (e.g., policies, designs and
plans) [Ben-Tal et al., 2009], resulting in influences on the
considered performance metric (e.g., cost, utility and reliabil-
ity). Robustness metrics function as a transformation of the
performance metrics under these future conditions.
General Rule for Metric Selection. In general, the system
administrator can follow a series of steps to select the proper
metrics: (1) Which requirements of trustworthiness should
be assessed? (2) Who cares about the issue the most, e.g., the
system administrator, the users, the regulators, society etc.?
(3) Regarding each requirement, who are the major concerned
entities for each party (because different parties may have dif-
ferent concerns, e.g., system admins care about performance
while users care about privacy), e.g., consistent performance,
protected data, equal performance? (4) What is the targeting
or common adversary? (5) What are the available data re-
sources to compute the selected metrics? (6) What is the dif-
ficulty and cost of the metric assessment? (7) Will the metrics
stay valid over time?
Nevertheless, no matter how proper the selected metrics
are, they are still only estimations and will not fully encom-
pass all the desired TAI requirements accurately. Addition-
ally, if maximizing the selected metrics became the major
model optimization logic, the model may perform well in
terms of the TAI metrics but fall short of the original model
goal(s). Therefore, periodic user studies are recommended
to continuously monitor the system compliance with the TAI
metrics. Adaption of the metric selection or standard can
be made accordingly to strike a balance between the original
goal and trustworthiness.
3 TAI Metrics in the Existing Cyberspace
Although existing TAI guidelines provide assessment met-
rics, the metrics are often quite high level and thus for in-
dividual providers to find or create detailed definitions (the
definition of a metric may vary in different fields) is not easy.
Moreover, different domains have different performance pri-
orities. Thus, the selection of TAI metrics should naturally
also vary. Future ecosystems, with more complexity includ-
ing in the user experience, only exacerbate these issues and
pose more challenges for TAI metric selection. Therefore,
we first examine TAI metrics in the current cyberspace and
summarize lessons for future ecosystems. In this section, we
outline TAI metrics in computing and networking, two basic
components of cyberspace that leverage AI.
We find that some metrics are widely used in varying ways
across different domains, including regression model met-
rics, including mean absolute error (MAE), normalized MAE
(NMAE), mean squared error (MSE) and root MSE (RMSE),
as well as ranking metrics, e.g., hit ratio, precision, recall,
specificity (true negative rate), F-score, discounted cumula-
tive gain (DCG), and their varieties. As they usually appear
as a group, we refer to them as regression model metrics and
ranking metrics throughout the survey.
3.1 Computing
This section focuses on the computing domain. We choose
two representative usecases: search engine ranking (SER)
and recommendation systems (RecSys), to illustrate the com-
mon TAI metrics. These usecases impact huge numbers of
users (billions) thus are major concerns in terms of TAI.
Nowadays, SER algorithms consider many factors such as
dwell time, page relevance, content quality, and so on. When
a search engine presents results, it typically records or cal-
culates such factors based on pre-defined policies, and treat
these as implicit proof of user interest. Therefore, the user
interaction with the ranking results is critical for training the
learning to rank (LR) models. Similarly, RecSys incorpo-
rates a number of machine learning techniques and has been
widely used in online media platforms, online shopping, and
social networks. A RecSys normally collects users’ histori-
cal choices for supervised learning (e.g., classifying items as
recommended or not) or unsupervised learning (e.g., matrix
factorization techniques common in collaborative filtering) to
learn and predict the user interest in items.
SER and RecSys provide sorted results to users aiming to
show results that match the user’s search input or recommend
items of user interest. However, when a user clicks on the top-
ranked link on the result page determining whether the selec-
tion is simply because the result is top ranked/recommended
or really the most relevant/interesting is difficult, as discov-
ered by the researchers [Joachims et al., 2017]. Since top
results have higher chances of being selected thus potentially
increasing revenues, it is important to guarantee the trustwor-
thiness of the presented results for the benefit of users.
Fairness Fairness is a major concern for SER and RecSys.
For instance, SERs sometimes are found to systematically fa-
vor certain sites over others in the results, thus distorting the
objectiveness of the results and degrading user trust [Tavani,
2012]. Additionally, in RecSys, the number of recommen-
dations is often fixed, therefore there are strong incentives to
promote products with greater commercial benefit instead of
fair recommendations based on ethical data mining.
Besides commercial incentives, implicit biases based on
ethnicity, gender, age, community and so on, that widely ex-
ist in society, are often reflected in the big data collected from
the Internet. Machine learning models thus often adopt these
biases when being trained on the bias-embedded datasets.
Technical flaws during data collection, sampling, and model
design can further exacerbate unfairness by introducing edge
cases, sampling bias, and temporal bias. Therefore, the mea-
surement of fairness with proper TAI metrics is critical for
both SER and RecSys to guarantee that the users receive neu-
tral and impartial services.
A common strategy of metric selection is to focus on one
factor and measure the deviation from the equality of that fac-
tor. For example, SER normally focuses on the (potential)
attention items receive from users in terms of factors such
as click-through rates, exposure, or inferences of the con-
tent relevance. The deviation from equality for these fac-
tors can then be quantified via disparate impact, disparate
exposure, disparate treatment [Singh and Joachims, 2018;
Castillo, 2019; Zehlike and Castillo, 2020], and inequity of
attention [Biega et al., 2018]. RecSys has also employed
similar metrics for fairness, such as bias disparity, aver-
age disparity, and score disparity [Tsintzou et al., 2018;
Leonhardt et al., 2018]. Kullback–Leibler (KL)-divergence
has also been employed with ad-hoc adaptions to measure the
fairness in SER [Geyik et al., 2019].
Other issues also affect the definition and selection of met-
rics. For instance, fairness can refer to disparate treatment
of individuals and of demographic groups, commonly termed
as individual fairness [Rastegarpanah et al., 2019]and group
fairness [Kamishima et al., 2012]. The former can be seen as
a special case of the latter where the group size equals one.
We further discuss more similar matters in Section 4.
Privacy Service providers are consistently improving the
user experience by providing personalized service using ma-
chine learning models trained with data about users’ personal
behavior and interests. A common method to acquire such
data is to request permission to collect data when associ-
ating the search engine or RecSys services with a user ac-
count, e.g., Google and Youtube can be associated with the
user’s Google account. While improving user experience, this
also raises privacy issues as personal information is sent to
a remote server [Xu et al., 2007]. Considering the volume
and detail of data current systems collect, privacy concerns
should be taken seriously [Jeckmans et al., 2013]. Addition-
ally, companies often have financial incentives that conflict
with protecting user privacy. For example, there has been a
continuous series of privacy concerns over Google services.
In 2012, Google changed its privacy policy to enable shar-
ing data across a wide variety of services 1. While in 2016,
Google quietly dropped its ban on personally-identifiable in-
formation in its DoubleClick ad service 2.
A common measurement logic is to assess the unwilling
exposure of private information. For unstructured data like
browsing history and email, entropy can provide a measure
of unique information and quantify the amount of exposed
private information [Agrawal and Aggarwal, 2001]. Though,
in practice, privacy preservation techniques can affect model
performance. Therefore, privacy is commonly assessed as
a multi-objective optimization problem. For example, cryp-
tographic protocols, differential privacy, and anonymization
approaches assess privacy preservation via trade-off measure-
ments between privacy exposure (controlled by some charac-
teristics of protocols or algorithms) and model performance
using regression model metrics and ranking metrics [Xu et
al., 2007; McSherry and Mironov, 2009; Xin and Jaakkola,
2014].
Robustness As mentioned, enormous volumes of data are
continuously generated online, thus models often require
1https://policies.google.com/privacy/archive/20120301
2https://thetechportal.com/2016/10/21/
google-now-personal-web-tracking-ads/
quite significant time and resources for retraining or incre-
mental learning and therefore cannot always be done timely.
This slow retraining and learning can result in issues after
sudden data shifts. In addition to real data shifts, the prob-
lem of spamdexing, namely when users enter fake ratings
to manipulate the ranking results, still exists in SER and re-
lated attacks are also common in collaborative filtering Rec-
Sys nowadays, dubbed as shilling attacks or profile injection
attacks [Lam and Riedl, 2004; Williams et al., 2007]. Finally,
malicious users may intentionally apply small targeted per-
turbations to datasets which can severely impact model per-
formance, e.g., image classification tasks in multimedia rec-
ommender systems [Moosavi-Dezfooli et al., 2017]. Overall,
an SER or RecSys is considered robust if not significantly
affected by attacks like spamdexing and perturbations or dra-
matically data shifts [Aggarwal, 2016].
A common measurement method is to assess the model
performance in the face of varying attacks or data shifts
using ranking metrics [Wang et al., 2013; Li et al., 2009;
Bailey et al., 2017; Tang et al., 2019]and regression model
metrics [O’Mahony et al., 2004; Lam and Riedl, 2004;
Mobasher et al., 2006; Mobasher et al., 2007; Cheng and
Hurley, 2010; Gunes et al., 2014].
3.2 Networking
This section focuses on the networking domain and illustrates
several example networking problems where different TAI
metrics are in use. These examples are relevant for both wire-
less and wired networks and cover several different network
layers. In contrast to the computing domain, most network-
ing research considers only a single TAI metric area (as oth-
ers are often considered irrelevant to the problem or out of
scope); thus, each example problem also describes only a sin-
gle TAI metric area. Relatedly, the metrics are more hetero-
geneous and ad-hoc than in the computing domain because
TAI is very new in the networking domain; therefore, more
detail and context are provided for each metric.
Wireless networking fairness. Radio resource allocation
in wireless networks is a well-known networking problem
with significant AI research and a major fairness component.
Specifically, in the LTE context, this problem manifests as the
allocation of physical resource blocks to specific user equip-
ment on the sector level. Fairness in this context means en-
suring that certain users are not starved of resources. Over-
all, such a problem is a multi-objective optimization prob-
lem with fairness as one objective with others such as high
overall system performance (throughput). As network com-
plexity and application diversity have risen, simple analytic or
heuristic scheduling solutions (e.g., round-robin, proportional
fair, and best CQI) are seen as potentially insufficient, and re-
search has turned to reinforcement learning (RL) to solve the
problem.
The fairness aspect in RL is thus embedded in the reward
function as this function directs the learning. In the simplest
cases, this fairness component of the reward function can be
a weighted version of a traditional network fairness metric,
such as Jain’s fairness index or entropy, or a custom fair-
ness metric resulting from reward engineering for the specific
problem. The basic Jain’s fairness index is defined for a single
type of network quality of service (QoS) measure (typically
user throughput). Thus, the index does not consider further
QoS measures (such as delay or packet loss) or users with
different applications (and thus different QoS requirements).
This index can be analogized to the independence group of
algorithmic fairness measures as the index is blind (to such
characteristics) and thus independent.
In more complex cases, such as with multiple QoS mea-
sures, different approaches are possible. For example Comsa
et al. [2019]considers fairness (within a group of users of
the same app) through a measure of the total sum of user-
QoS requirement combinations (e.g., user Awith throughput
threshold of X) met in a given period (one TTI). The authors
then further generalizes this by considering many user groups
with each group using another application. Specifically, the
total fairness is a weighted sum of the intra-app-group fair-
nesses, with the weighting being a learnable (through RL)
prioritization of the applications. As another example, Al-
Tam et al. [2020]use a variant of discounted best-CQI (a
common network fairness model) as the reward function with
the discounting based on the well known min-max ratio fair-
ness measure.
Network traffic privacy. Network traffic classification is
a major networking area, especially for mobile network op-
erators as knowing the specific network traffic mixture sup-
ports many network tasks. For example, traffic mixture
knowledge enables network optimizations including optimiz-
ing user QoS and QoE and better traffic volume predic-
tion. However, more nefariously, traffic classification can
also impinge on user privacy as entities (such as compa-
nies and governments) can use such classification to iden-
tify, for example, users of specific apps or websites (like
those used by political dissidents). These network traffic
classifiers often use a variety of ML and AI methods, in-
cluding RF, SVM, and DNN, with and without handcrafted
traffic features. Thus, to counteract network traffic clas-
sification, researchers are applying both AI methods such
as generative adversarial networks (GANs) [Li et al., 2019;
Fathi-Kazerooni and Rojas-Cessa, 2020; Hou et al., 2020]
and non-AI methods, such as adaptive packet padding [Pin-
heiro et al., 2020]and optimized dummy packet injection
[Shan et al., 2021], to intelligently obfuscate the network traf-
fic. These research studies primarily use related accuracy-
based metrics to assess privacy improvement.
Some works use the misclassification rate of the classifiers
on the obfuscated traffic [Shan et al., 2021; Hou et al., 2020].
Relatedly, other works use the differences in the classifica-
tion accuracies of the classifier on the original and obfus-
cated traffic [Pinheiro et al., 2020]. Goal-wise, certain stud-
ies analyze the classifier accuracy, recall, and precision on
traffic of one type disguised (by a GAN) to look like traffic
of a different (target) type (rather than a goal of just gen-
eral obfuscation) [Fathi-Kazerooni and Rojas-Cessa, 2020;
Hou et al., 2020]. Finally, Li et al. [2019]uses both indis-
tinguishability under Classification Attack (IND-CA) and the
differences in AUC of the ROC curve of the classifier on the
original and obfuscated traffic. IND-CA quantifies how dis-
tinguishable the traffic is when considering two traffic types
with equal shares (each representing 50% of traffic). Specif-
ically, IND-CA represents the normalized benefit of the clas-
sification with zero meaning a random guess and, in contrast,
one meaning full certainty.
Networking congestion control robustness. Similar to the
resource allocation problem, the networking congestion con-
trol problem also lends well to an RL approach. The issue
also deals with significant multi-layer network complexity
(where simpler heuristics are insufficient). Specifically, the
major target is TCP congestion control with RL adapting the
receive window size. A potential benefit to the RL approach
is its robustness to different network conditions.
However, this is sometimes only the case if those network
conditions were part of the training regime (for offline RL).
Thus, robustness testing with conditions that both span and
extend beyond the training regime is important. Some of the
TCP RL works use regret-based metrics with a baseline sce-
nario containing only network conditions from the training
range. The metric is then defined as the performance gap
between this baseline and scenarios that include conditions
beyond the training regime [He et al., 2021].
In other cases where robustness to different conditions is
built-in to the RL approach, the metric is simply the perfor-
mance gap in the diverse conditions of this approach from
baseline approaches. Du et al. [2021], for example, use a
hybrid approach with traditional (heuristic) and RL parts to
improve robustness in both diverse wired and wireless net-
work situations. The approach proves better (in terms of
mean throughput and delay) than either approach alone in
such situations. Other works use statistical dispersion met-
rics such as standard deviation and confidence intervals to
illustrate the general robustness of the results (for example,
where stability is important). For instance, an RL approach
[Xiao et al., 2019]illustrates a 95% confidence interval that is
smaller than all the baselines for a specific performance mea-
sure (a fairness index); thus illustrating robustness to more
significant swings.
4 Lessons Learned and Research Agenda
By examining the TAI metric selection across computing and
networking domains, we can see that the definition and selec-
tion of TAI metrics for computing are more straightforward
than for networking. A significant reason is that the outputs of
many computing systems like recommendation systems and
search engines are more oriented towards end-users, e.g., the
results of ranking algorithms well match the needs of SER
which uses user data and show results directly to users.
In contrast, the learning algorithms in the networking con-
text usually result in intermediate metrics that serve to adapt
protocols or algorithms that eventually affect the target met-
rics. In other words, the output of the learning algorithms in
computing can often be directly used to assess user experi-
ence, while in networking there is normally an intermediate
model to transfer networking performance (controlled by AI)
to user experience. Thus, TAI metrics in networking require
more ad-hoc designs, definitions of usage and user context,
and targets of networking systems.
Currently, most computing and networking systems use
“functionality-driven design”, which we use as a contrast to
“user-centered design”, in the sense that the former focuses
more on pre-defined systematic performance metrics though
also sometimes considers user-related metrics, such as QoS
and QoE. Relatedly, TAI design and metric selection in such
systems also often focus on the most functionalities during
specific life-cycle phases. Ideally, TAI metric selection de-
mands more thorough considerations to guarantee trustwor-
thiness through the life cycle of usage. Furthermore, the cur-
rent mindset of TAI design and metric selection, restricted
by the aforementioned design philosophies, takes into con-
sideration only part of human cognition, specifically the con-
scious and concrete areas that can be more easily measured
and quantified, such as pattern recognition, language, atten-
tion, perception, and action. These are widely explored by AI
communities.
However, the exploration of the unconscious and abstract
areas of cognition, e.g., mental health and emotion, is just
beginning. Methodological limits is a key reason for this, e.g.,
lack of devices and theories to accurately capture bioelectrical
signals and convert these signals to emotional statuses.
Trustworthiness itself consists of cognitive, emotional and
behavioral factors, since trustworthiness is a user-oriented
term. This aspect will play an increasingly important role
when “user-centered design” dominates future cyberspace,
replacing the current “functionality-driven design”. In the fu-
ture, considering the unconscious and abstract cognition ar-
eas will be vital to guarantee TAI. These parts are hard to
quantify and might remain so even with advanced techniques
in sensor-enabled immersive cyberspace. Therefore, other as-
sessment methods may be required for TAI in such immersive
cyberspaces. This is discussed in the remaining paragraphs.
From ad-hoc to systematic metric selection Although we
presented an outlook on how TAI metrics are selected from
the surveyed literature in computing and networking do-
mains, it can be more complicated when system developers
try to select or define TAI metrics in reality. Because achiev-
ing trustworthiness highly relies on the contextual environ-
ment besides the AI system itself, the selection of metrics
requires a holistic and systemic consideration encompass-
ing all processes within the system’s socio-technical context
throughout its entire life cycle.
Therefore, every system, even if it is an identical copy
of another, may require different TAI metrics or some met-
ric adaptions due to the differences in the deployed contex-
tual environments and life cycles. Moreover, the selection
of metrics also depends on the granularity of the concerned
target (pointwise, pairwise, listwise) and the operation phase
(pre-processing, in-processing, post-processing). Thus, even
for straightforward computing tasks, developers need delicate
considerations about the whole context and potential scenar-
ios to fully justify the selection of TAI metrics.
TAI metric selection for immersive cyberspace Current
cyberspace is evolving as new technologies develop, and the
advent of immersive cyberspace will be enabled by AI in a
greater extent. For example, since 2021, the metaverse, also
known as the immersive Internet, has risen to public attention,
thanks to Facebook’s rebranding, and worldwide academic
and industrial promotion. In the metaverse, AI continues to
play a core role as the foundation of several key technolo-
gies, namely, computer vision, augmented & virtual reality
(AR/VR), data mining, and robotics [Lee et al., 2021]. The
most critical difference between the metaverse and current cy-
berspace is that in the metaverse, human users are absorbed
into the projected blended virtual-physical world without ex-
plicit exit points instead of just standing by as external inter-
actors. As such, any consequences caused by AI misbehavior
could be significantly worse and hence severely impact hu-
man users’ welfare.
For example, VR technologies are capable of recording
richer personal data, such as eye movements and emotional
reactions, which could be deployed in threatening ways pow-
ered by AI techniques to manipulate users’ beliefs, emo-
tions, and behaviors [Spiegel, 2018]. In contrast, AR requires
strong contextual awareness, in terms of users, their adjacent
environments, and social interactions, to augment the phys-
ical world [Lam et al., 2021]. As such, users have to share
egocentric views of various contexts, e.g., Project EGO4D3.
In other words, users have to build trust with virtual-
physical blended cyberspace mediated by computing sys-
tems, as the users’ daily interactions with physical worlds
and people are recorded in an unprecedentedly massive scale.
Meanwhile, users will require an easy-to-interpret score, e.g.,
AI Trust Score [Wang and Moulden, 2021], to judge AI trust-
worthiness over time (instead of a single snapshot), while AI
is resilient to the occurrence of glitches and afterwards able
to recover trusts with users. Furthermore, the “user-centered”
features of the metaverse may bring important changes to TAI
and TAI metric selection. In current cyberspace, AI-enabled
applications and human users interact but are still signifi-
cantly and explicitly separated. Hence the measure of TAI
mainly focuses on system performance and technical metrics.
In the metaverse, however, user-representative avatars,
cognitive emotional-interactive products, and other similar
humanoids will play vital roles in improving a user’s feel-
ing of involvement to seamlessly experience the blended
virtual-physical world. More importantly, the aforemen-
tioned avatars and humanoids will collaborate with human
users. Thus, user-centered TAI metrics, such as those focus-
ing on cognition, sentiment, and psychology, with sensor-
enabled monitoring in the metaverse, will become a new
driver for understanding robustness, privacy and fairness.
Emerging techniques may have the potential to tackle the
challenge through understanding the internal states of users.
For example, the state-of-the-art Brain-Computer Interface
(BCI) can estimate the user’s current emotion, attention, or
fatigue level to some extent by monitoring the bioelectri-
cal signals that reflect brain activity. These signals can be
recorded by a device like an electroencephalogram [Shatilov
et al., 2021]. These emerging techniques may allow for quan-
titatively measuring the abstract metrics that currently rely on
limited-scale qualitative experiments (often based on user in-
terviews).
Nevertheless, these techniques are still immature. More-
3https://ego4d-data.org/
over, the practicality and applicability of the techniques
maybe be limited, as they normally require the users to wear
additional devices, which are often inconvenient. Nowadays,
immersive headsets (AR/VR) have similar issues [Lee et al.,
2022]. Therefore, requirements for qualitative measures like
user studies for TAI assessment, e.g., eliciting user require-
ments for trust-guarantee AI services, might be reasonable.
However, the current approaches for understanding users, to a
large extent, are costly and time-consuming. Thus, this time-
liness issue is a current challenge to be solved.
TAI governance Currently, the governance of the trustwor-
thiness of AI systems is mostly left in the hands of the service
providers themselves and some third-party companies and
government institutes. As user awareness of TAI increases, a
key challenge for TAI governance is how to assess and guar-
antee fairness, privacy, and robustness in a more standard-
ized, transparent, and systematic way. Moreover, the coming
metaverse will integrate more AI services into daily life on an
unprecedentedly massive scale. Governance by each individ-
ual service provider creates coordination and standardization
problems and thus provides no guarantee of equity in trust-
worthiness standards. Additionally, the burden might be too
large for a limited number of third party companies or govern-
ment institutes. Accordingly, building an autonomous gov-
ernance platform, “meta-TAI”, to govern TAI performance,
might be worth exploring. The meta-TAI platform could be
collaboratively governed by a number of trustworthy insti-
tutes authorized by the involved countries, as well as offering
TAI scores for various providers of AI services and their indi-
vidual solutions. As such, the platform can save manual effort
while ensuring the equity of standards across various phases
of a user-AI interaction cycle.
5 Conclusion
This survey discussed metrics for TAI and further examined
the aspects of fairness, privacy and robustness in the com-
puting and networking domains. The existing metrics are
mainly driven by system functionalities and efficacy with less
emphasis on user-centered factors. Meanwhile, the ad-hoc
metric selection causes sub-optimal results in building trust-
worthiness with users. We revisited the TAI domain to lay
out a research agenda that will assist researchers working
on TAI and immersive cyberspace to contextualize and fo-
cus their efforts. We note that AI will become an indispens-
able driver of immersive cyberspace and that users will inter-
act with AI-enabled services in this virtual-physical blended
world under the premise that user trust is essential to the wide
adoption of such services. Therefore, we call for a user-
centered paradigm of building trustworthiness beyond sole
system measurements and considering cognitive and affective
factors.
References
[Adadi and Berrada, 2018]A. Adadi and M. Berrada. Peek-
ing inside the black-box: A survey on explainable artificial
intelligence (xai). IEEE Access, 6:52138–52160, 2018.
[Aggarwal, 2016]C. C. Aggarwal. Recommender systems,
volume 1. Springer, 2016.
[Agrawal and Aggarwal, 2001]D. Agrawal and C. C. Ag-
garwal. On the design and quantification of privacy pre-
serving data mining algorithms. In Proc. of the 20th
ACM SIGMOD-SIGACT-SIGART Symp. on Principles of
database systems, pages 247–255, 2001.
[Al-Tam et al., 2020]F. Al-Tam, N. Correia, and J. Ro-
driguez. Learn to schedule (leasch): A deep reinforcement
learning approach for radio resource scheduling in the 5g
mac layer. IEEE Access, 8:108088–108101, 2020.
[Bailey et al., 2017]P. Bailey, A. Moffat, F. Scholer, and
P. Thomas. Retrieval consistency in the presence of query
variations. In Proc. of the 40th Inter. ACM SIGIR, pages
395–404, 2017.
[Ben-Tal et al., 2009]A. Ben-Tal, L. El Ghaoui, and A. Ne-
mirovski. Robust optimization. Princeton Uni. press, 2009.
[Biega et al., 2018]A. J. Biega, K. P. Gummadi, and
G. Weikum. Equity of attention: Amortizing individual
fairness in rankings. In The 41st Inter. ACM SIGIR Conf.,
pages 405–414, 2018.
[Castillo, 2019]C. Castillo. Fairness and transparency in
ranking. In ACM SIGIR Forum, volume 52, pages 64–71.
ACM USA, 2019.
[Cheng and Hurley, 2010]Z. Cheng and N. Hurley. Robust
collaborative recommendation by least trimmed squares
matrix factorization. In 2010 22nd IEEE Inter. Conf. on
Tools with AI, volume 2, pages 105–112. IEEE, 2010.
[Cheng et al., 2016]H.-T. Cheng, L. Koc, J. Harmsen, et al.
Wide & deep learning for recommender systems. In Proc.
of the 1st WKSP on deep learning for recommender sys-
tems, pages 7–10, 2016.
[Coms
,aet al., 2019]I.-S. Coms
,a, R. Trestian, G.-M.
Muntean, and G. Ghinea. 5mart: A 5g smart scheduling
framework for optimizing qos through reinforcement
learning. IEEE Trans. on Network and Service Mgt.,
17(2):1110–1124, 2019.
[Cui et al., 2006]G. Cui, M.-L. Wong, and H.-K. Lui. Ma-
chine learning for direct marketing response models:
Bayesian networks with evolutionary programming. Mgt.
Sci., 52(4):597–612, 2006.
[Du et al., 2021]Z. Du, J. Zheng, H. Yu, L. Kong, and
G. Chen. A unified congestion control framework for di-
verse application preferences and network conditions. In
Proc. of the 17th Inter. Conf. on emerging Networking Ex-
periments and Tech., pages 282–296, 2021.
[European Union, 2012]European Union. Eu charter of fun-
damental rights. 2012.
[Fathi-Kazerooni and Rojas-Cessa, 2020]S. Fathi-
Kazerooni and R. Rojas-Cessa. Gan tunnel: network
traffic steganography by using gans to counter internet
traffic classifiers. IEEE Access, 8:125345–125359, 2020.
[Geyik et al., 2019]S. C. Geyik, S. Ambler, and K. Kentha-
padi. Fairness-aware ranking in search & recommendation
systems with application to linkedin talent search. In Proc.
of the 25th ACM SIGKDD, pages 2221–2231, 2019.
[Gunes et al., 2014]I. Gunes, C. Kaleli, A. Bilge, and H. Po-
lat. Shilling attacks against recommender systems: a com-
prehensive survey. AI Review, 42(4):767–799, 2014.
[He et al., 2021]B. He, J. Wang, Q. Qi, et al. Deepcc: Multi-
agent deep reinforcement learning congestion control for
multi-path tcp based on self-attention. IEEE Trans. on Net-
work and Service Mgt., 18(4):4770–4788, 2021.
[Heaven, 2021]W. D. Heaven. The department of defense is
issuing ai ethics guidelines for tech contractors. 2021.
[HLEGAI, 2019]HLEGAI. Ethics guidelines for trustwor-
thy ai. B-1049 Brussels, 2019.
[Hou et al., 2020]C. Hou, G. Gou, et al. Wf-gan: Fighting
back against website fingerprinting attack using adversar-
ial learning. In 2020 IEEE Symp.on Comp. and Commun.
(ISCC), pages 1–7. IEEE, 2020.
[Jeckmans et al., 2013]A. JP Jeckmans, M. Beye, Z. Erkin,
et al. Privacy in recommender systems. In Social media
retrieval, pages 263–281. Springer, 2013.
[Joachims et al., 2017]T. Joachims, L. Granka, B. Pan,
H. Hembrooke, and G. Gay. Accurately interpreting click-
through data as implicit feedback. In ACM SIGIR Forum,
volume 51, pages 4–11. ACM USA, 2017.
[Jordan and Mitchell, 2015]M. I. Jordan and T. M Mitchell.
Machine learning: Trends, perspectives, and prospects.
Science, 349(6245):255–260, 2015.
[Kamishima et al., 2012]T. Kamishima, S. Akaho, H. Asoh,
et al. Enhancement of the neutrality in recommendation.
In Decisions@ RecSys, pages 8–14. Citeseer, 2012.
[Lam and Riedl, 2004]S. K. Lam and J. Riedl. Shilling rec-
ommender systems for fun and profit. In Proc. of the 13th
Inter. Conf. on WWW, pages 393–402, 2004.
[Lam et al., 2021]K. Y. Lam, L. H. Lee, and P. Hui. A2W:
Context-Aware Recommendation System for Mobile Aug-
mented Reality Web Browser, page 2447–2455. ACM,
USA, 2021.
[Laufer, 1973]R. S. Laufer. Some analytic dimensions of
priacy. In Architectural psychology, Proc. of the Lund
Conf., Lund: Studentlitteratur, 1973.
[Lee et al., 2021]L.-H. Lee, T. Braud, P. Zhou, et al. All
one needs to know about metaverse: A complete survey on
technological singularity, virtual ecosystem, and research
agenda. arXiv:2110.05352, 2021.
[Lee et al., 2022]L.-H. Lee, T. Braud, S. Hosio, and P. Hui.
Towards augmented reality driven human-city interaction:
Current research on mobile headsets and future challenges.
ACM CSUR, 54:1 – 38, 2022.
[Leonhardt et al., 2018]J. Leonhardt, A. Anand, and
M. Khosla. User fairness in recommender systems.
In Compan. Proc. of the The Web Conf. 2018, pages
101–102, 2018.
[Li et al., 2009]X. Li, F. Li, S. Ji, Z. Zheng, et al. Incorpo-
rating robustness into web ranking evaluation. In Proc. of
the 18th ACM Conf. on Info. and Knowledge Mgt., pages
2007–2010, 2009.
[Li et al., 2019]J. Li, L. Zhou, H. Li, L. Yan, and H. Zhu.
Dynamic traffic feature camouflaging via generative ad-
versarial networks. In 2019 IEEE Conf. on Comm. and
Network Security (CNS), pages 268–276. IEEE, 2019.
[Lin et al., 2011]W.-Y. Lin, Y.-H. Hu, and C.-F. Tsai. Ma-
chine learning in financial crisis prediction: a survey. IEEE
Trans. on Systems, Man, and Cybernetics, Part C (Appli-
cations and Reviews), 42(4):421–436, 2011.
[Maier et al., 2016]H. R. Maier, J. HA Guillaume, H. van
Delden, et al. An uncertain future, deep uncertainty, sce-
narios, robustness and adaptation: How do they fit to-
gether? Environ. Modelling & Soft., 81:154–164, 2016.
[Mantelero, 2018]A. Mantelero. Ai and big data: A
blueprint for a human rights, social and ethical impact as-
sessment. Comp. Law & Security Review, 34(4):754–772,
2018.
[McSherry and Mironov, 2009]F. McSherry and I. Mironov.
Differentially private recommender systems: Building pri-
vacy into the netflix prize contenders. In Proc. of the 15th
ACM SIGKDD, pages 627–636, 2009.
[Mobasher et al., 2006]B. Mobasher, R. Burke, and J. J.
Sandvig. Model-based collaborative filtering as a defense
against profile injection attacks. In AAAI, volume 6, page
1388, 2006.
[Mobasher et al., 2007]B. Mobasher, R. Burke, R. Bhau-
mik, and C. Williams. Toward trustworthy recommender
systems: An analysis of attack models and algorithm ro-
bustness. ACM TOIT, 7(4):23–es, 2007.
[Moosavi-Dezfooli et al., 2017]S.-M. Moosavi-Dezfooli,
A. Fawzi, O. Fawzi, and P. Frossard. Universal adversarial
perturbations. In Proc. of the IEEE Conf. on CVPR, pages
1765–1773, 2017.
[Nissenbaum, 2004]H. Nissenbaum. Privacy as contextual
integrity. Wash. L. Rev., 79:119, 2004.
[Norgeot et al., 2019]B. Norgeot, B. S. Glicksberg, and A. J.
Butte. A call for deep-learning healthcare. Nature
medicine, 25(1):14–15, 2019.
[O’Mahony et al., 2004]M. O’Mahony, N. Hurley, N. Kush-
merick, and G. Silvestre. Collaborative recommendation:
A robustness analysis. ACM TOIT, 4(4):344–377, 2004.
[Paluck et al., 2019]E. L. Paluck, S. A. Green, and D. P.
Green. The contact hypothesis re-evaluated. Behavioural
Public Policy, 3(2):129–158, 2019.
[Pinheiro et al., 2020]A. J. Pinheiro, P. F. de Araujo-Filho,
J. de M. Bezerra, and D. R. Campelo. Adaptive packet
padding approach for smart home networks: A tradeoff
between privacy and performance. IEEE Internet of Things
J., 8(5):3930–3938, 2020.
[Rastegarpanah et al., 2019]B. Rastegarpanah, K. P. Gum-
madi, and M. Crovella. Fighting fire with fire: Using an-
tidote data to improve polarization and fairness of recom-
mender systems. In Proc. of the 12th ACM Inter. Conf. on
WSDM, pages 231–239, 2019.
[Shan et al., 2021]S. Shan, A. N. Bhagoji, H. Zheng, and
B. Y. Zhao. Patch-based defenses against web fingerprint-
ing attacks. In Proc. of the 14th ACM WKSP on AI and
Security, pages 97–109, 2021.
[Shatilov et al., 2021]K. A. Shatilov, D. Chatzopoulos, L.-
H. Lee, and P. Hui. Emerging exg-based nui inputs in
extended realities: A bottom-up survey. ACM Trans. In-
teract. Intell. Syst., 11(2), jul 2021.
[Shiue et al., 2018]Y.-R. Shiue, K.-C. Lee, and C.-T. Su.
Real-time scheduling for a smart factory using a reinforce-
ment learning approach. Computers & Industrial Eng.,
125:604–614, 2018.
[Singh and Joachims, 2018]A. Singh and T. Joachims. Fair-
ness of exposure in rankings. In Proc. of the 24th ACM
SIGKDD, pages 2219–2228, 2018.
[Smuha, 2019]N. A. Smuha. The eu approach to ethics
guidelines for trustworthy artificial intelligence. CRi-
Comp. Law Review Inter., 2019.
[Spiegel, 2018]J. S. Spiegel. The ethics of virtual reality
technology: Social hazards and public policy recommen-
dations. Sci. and Eng. ethics, 24(5):1537–1550, 2018.
[Tang et al., 2019]J. Tang, X. Du, X. He, F. Yuan, et al. Ad-
versarial training towards robust multimedia recommender
system. IEEE Trans. on KDE, 32(5):855–867, 2019.
[Tavani, 2012]H. Tavani. Search engines and ethics. 2012.
[Torresen, 2018]J. Torresen. A review of future and ethical
perspectives of robotics and ai. Frontiers in Robotics and
AI, 4:75, 2018.
[Tsintzou et al., 2018]V. Tsintzou, E. Pitoura, and
P. Tsaparas. Bias disparity in recommendation sys-
tems. arXiv:1811.01461, 2018.
[Wang and Moulden, 2021]J. Wang and A. Moulden. AI
Trust Score: A User-Centered Approach to Building, De-
signing, and Measuring the Success of Intelligent Work-
place Features. ACM, USA, 2021.
[Wang et al., 2013]Y. Wang, L. Wang, Y. Li, et al. A theo-
retical analysis of ndcg ranking measures. In Proc. of the
26th Conf. COLT, volume 8, page 6. Citeseer, 2013.
[Williams et al., 2007]C. A. Williams, B. Mobasher, and
R. Burke. Defending recommender systems: detection of
profile injection attacks. Service Oriented Comp. & Apps.,
1(3):157–170, 2007.
[Xiao et al., 2019]K. Xiao, S. Mao, and J. K. Tugnait. Tcp-
drinc: Smart congestion control based on deep reinforce-
ment learning. IEEE Access, 7:11892–11904, 2019.
[Xin and Jaakkola, 2014]Y. Xin and T. Jaakkola. Control-
ling privacy in recommender systems. NeurIPS, 2014.
[Xu et al., 2007]Y. Xu, K. Wang, B. Zhang, and Z. Chen.
Privacy-enhancing personalized web search. In Proc. of
the 16th Inter. Conf. on WWW, pages 591–600, 2007.
[Zehlike and Castillo, 2020]M. Zehlike and C. Castillo. Re-
ducing disparate exposure in ranking: A learning to rank
approach. In Proc. of The Web Conf. 2020, pages 2849–
2855, 2020.
ResearchGate has not been able to resolve any citations for this publication.
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Al-Tam et al., 2020] F. Al-Tam, N. Correia, and J. Rodriguez. Learn to schedule (leasch): A deep reinforcement learning approach for radio resource scheduling in the 5g mac layer. IEEE Access, 8:108088-108101, 2020. [Bailey et al., 2017] P. Bailey, A. Moffat, F. Scholer, and P. Thomas. Retrieval consistency in the presence of query variations. In Proc. of the 40th Inter. ACM SIGIR, pages 395-404, 2017.
Equity of attention: Amortizing individual fairness in rankings
  • Ben-Tal
Ben-Tal et al., 2009] A. Ben-Tal, L. El Ghaoui, and A. Nemirovski. Robust optimization. Princeton Uni. press, 2009. [Biega et al., 2018] A. J. Biega, K. P. Gummadi, and G. Weikum. Equity of attention: Amortizing individual fairness in rankings. In The 41st Inter. ACM SIGIR Conf., pages 405-414, 2018.
5mart: A 5g smart scheduling framework for optimizing qos through reinforcement learning
  • Coms
Coms, a et al., 2019] I.-S. Coms, a, R. Trestian, G.-M. Muntean, and G. Ghinea. 5mart: A 5g smart scheduling framework for optimizing qos through reinforcement learning. IEEE Trans. on Network and Service Mgt., 17(2):1110-1124, 2019.
Fairness-aware ranking in search & recommendation systems with application to linkedin talent search
et al., 2006] G. Cui, M.-L. Wong, and H.-K. Lui. Machine learning for direct marketing response models: Bayesian networks with evolutionary programming. Mgt. Sci., 52(4):597-612, 2006. [Du et al., 2021] Z. Du, J. Zheng, H. Yu, L. Kong, and G. Chen. A unified congestion control framework for diverse application preferences and network conditions. In Proc. of the 17th Inter. Conf. on emerging Networking Experiments and Tech., pages 282-296, 2021. [European Union, 2012] European Union. Eu charter of fundamental rights. 2012. [Fathi-Kazerooni and Rojas-Cessa, 2020] S. Fathi-Kazerooni and R. Rojas-Cessa. Gan tunnel: network traffic steganography by using gans to counter internet traffic classifiers. IEEE Access, 8:125345-125359, 2020. [Geyik et al., 2019] S. C. Geyik, S. Ambler, and K. Kenthapadi. Fairness-aware ranking in search & recommendation systems with application to linkedin talent search. In Proc. of the 25th ACM SIGKDD, pages 2221-2231, 2019. [Gunes et al., 2014] I. Gunes, C. Kaleli, A. Bilge, and H. Polat. Shilling attacks against recommender systems: a comprehensive survey. AI Review, 42(4):767-799, 2014. [He et al., 2021] B. He, J. Wang, Q. Qi, et al. Deepcc: Multiagent deep reinforcement learning congestion control for multi-path tcp based on self-attention. IEEE Trans. on Network and Service Mgt., 18(4):4770-4788, 2021. [Heaven, 2021] W. D. Heaven. The department of defense is issuing ai ethics guidelines for tech contractors. 2021. [HLEGAI, 2019] HLEGAI. Ethics guidelines for trustworthy ai. B-1049 Brussels, 2019. [Hou et al., 2020] C. Hou, G. Gou, et al. Wf-gan: Fighting back against website fingerprinting attack using adversarial learning. In 2020 IEEE Symp.on Comp. and Commun. (ISCC), pages 1-7. IEEE, 2020. [Jeckmans et al., 2013] A. JP Jeckmans, M. Beye, Z. Erkin, et al. Privacy in recommender systems. In Social media retrieval, pages 263-281. Springer, 2013. [Joachims et al., 2017] T. Joachims, L. Granka, B. Pan, H. Hembrooke, and G. Gay. Accurately interpreting clickthrough data as implicit feedback. In ACM SIGIR Forum, volume 51, pages 4-11. ACM USA, 2017. [Jordan and Mitchell, 2015] M. I. Jordan and T. M Mitchell. Machine learning: Trends, perspectives, and prospects. Science, 349(6245):255-260, 2015. [Kamishima et al., 2012] T. Kamishima, S. Akaho, H. Asoh, et al. Enhancement of the neutrality in recommendation. In Decisions@ RecSys, pages 8-14. Citeseer, 2012.
Smuha. The eu approach to ethics guidelines for trustworthy artificial intelligence
  • Shiue
Shiue et al., 2018] Y.-R. Shiue, K.-C. Lee, and C.-T. Su. Real-time scheduling for a smart factory using a reinforcement learning approach. Computers & Industrial Eng., 125:604-614, 2018. [Singh and Joachims, 2018] A. Singh and T. Joachims. Fairness of exposure in rankings. In Proc. of the 24th ACM SIGKDD, pages 2219-2228, 2018. [Smuha, 2019] N. A. Smuha. The eu approach to ethics guidelines for trustworthy artificial intelligence. CRi-Comp. Law Review Inter., 2019.