series of consequences on the whole city and also may unintentionally attack
other models. Estimating the loss and effect of attacking a model and
functional dependency evaluation could be integral parts of the future AI-
based systems development life-cycle. We can expect more interest in
simulation works in this area soon.

  * 3.

_Real-Time Adversarial Attacks_.

This is another challenge for AI safety teams. There is a need to evaluate the
current techniques in generating poisoning data when only part of benign data
is available, i.e., streaming. How about the structures of defenders in real-
time environments?[341]

  * 4.

Future works may show more efforts in defining the rules on operating smart
cyber-systems and the accountability of the services providers and
operators[342].

  * 5.

_Unintentional attacks in smart waste and agriculture_ Smart waste and
agriculture mainly depend on a network of sensors that work in harder
conditions compared to some other fields such as transportation. In such a
scenario, the environments might be wet, humid, dirty, have different
temperatures, and may suffer from pollution. For example, the sensors attached
to the animals in large farms, sensors on trash bins, electrochemical sensors
for soil nutrients, etc, are subject to convey some noise besides the required
data due to the environmental effects. This could be an important source of
unintentional attacks that should be evaluated and taken into account in
future works.

  * 6.

_AI models detection and isolation techniques_ In [76], a technique of
abnormal vehicle behavior detection and isolation is applied on the object
level (i.e., vehicle) which may run several models to control the driving
tasks and traffic management. Evaluating the approach on a lower level, i.e.,
models, to detect and isolate the possibly attacked models might add value to
the overall safety. Developing guidelines for replacing suspected models or
defining alternatives in AI models’ maintenance plan could improve consumers’
trust.

  * 7.

_Robustness and safety metrics to be involved in the evaluation process of AI
models._ The current metrics to evaluate the performance of AI models could
take into account the factor of safety and the robustness of the model against
different types of attacks. AI models of high accuracy at testing time might
be the worse with a little noise added by attackers at production
environment[343]. This leads to the possible need of revisiting the AI models
evaluation policy before deployment. Thus, the agreement between the
stakeholders or services.

###  7.2 Smart City AI Interpretability

Although a lot of efforts have been made for the interpretation/explainability
of AI algorithms since the concept of explainable AI has been introduced.
However, there are still many aspects of explainable AI that need to be
analyzed. In this section, we provide some of the open issues and future
research directions in the domain.

####  7.2.1 Interpretation vs. Performance

Despite all the benefits it brings for all the stakeholders in different
application domains, there are some concerns about its impact on the
performance and the development process. It is believed that the efforts for
explainability will not only slow down the development process but also put
constraints on it, which might also hurt the performance (i.e., accuracy) of
the models [344]. For better interpretability AI models to be simple as
simpler the model more explainable is the causes of an underlying decision.
However, literature shows that usually complex AI algorithms (e.g., deep
learning) tend to be more accurate. The trade-off between explainability and
performance is believed to be optimized with better explainability methods,
which is one of the key research challenges in the domain [56, 345, 56].

####  7.2.2 Concepts and Evaluation Metrics

The literature still lacks a common ground, structure, and a unified concept
of explainability [56]. However, several efforts have been made in this
regard. For instance, Arrieta et al. [56] attempted to provide a common ground
or a reference point in this regard. According to them, the explainability of
an AI model refers to its ability to make its functioning (i.e., causes of its
decisions) clearer to an audience. The authors also emphasize the need and
definition of an evaluation metric or set of metrics for the evaluation and
comparison of AI models in terms of explainability and interpretation
capabilities.

####  7.2.3 Explanation of Deep Learning Models

Despite the sincere efforts made for explainable AI, there are still several
challenges hindering its success and adoption. One of the key challenges is
the interpretability of deep learning. In this regard, efforts are ongoing to
develop explainable deep learning techniques or applications. To this aim,
different visualization techniques are used to explain their reasoning steps,
which is expected to make them explainable and trustworthy.

####  7.2.4 Explainability and Adversarial AI

As detailed earlier, explainability and adversarial AI has a direct
connection. Explainability on the one side can guard against adversarial
attacks by differentiating between a genuine sample and an adversary while on
the other hand the information revealed by explainability techniques can be
used both to generate more effective adversarial attacks on AI algorithms
[56]. One of the interesting directions of research on explainable AI is to
analyze how effectively it can be used to guard against adversarial attacks.
There are already ongoing efforts in this direction as detailed in Section
3.2.

###  7.3 AI Ethics

Despite the significant progress AI ethics could make in a short period, many
issues still remain open and various challenges still need to be addressed by
future research. Below, we summarize the key points in this regard.

  * 1.

Due to its strongly interdisciplinary character and relatively young age, AI
ethics suffers from serious conceptual ambiguity. Many of the key terms have
fundamentally different and, sometimes even incompatible, meanings for
different people. For example, key terms like agent, autonomy, and
intelligence do not have the same meaning for moral philosophers and AI
engineers. For engineers, cars or weapons will be “autonomous” when they can
behave without direct human intervention. Moral philosophers, however, would
use the term “autonomous” exclusively for an entity that can define its own
laws or rules of behavior by itself [311]. To improve the AI moral discourse
and make it more efficient, there is a dire need for future research that will
enhance its conceptual clarity and standardize the primary and secondary
meanings of its key terms.

  * 2.

There is a need for exploring innovative ways to bridge the existing gaps
between academic research and policymaking on one hand and between
policymaking and the AI reality on the other hand. The questions raised and
addressed by the academics are sometimes too abstract and theoretical to be of
relevance for policymakers and those engaged in the AI business. Instead of
broad philosophical questions like “Will this contribute to human flourishing
or put human species at risk?,” policymakers are more interested in practical
questions like “Which harms should we expect if we are going to do this, and
how to mitigate or minimize these harms?.” Despite some good but still
seemingly exceptional instances, various researchers also warn that there is
hardly any touchable impact of ethics in general or policies and guidelines in
particular on the reality of the AI industry. Most of the time, large
companies are driven by economic logic and incentives rather than by value or
principle-based ethics [206, 346].

  * 3.

The moral discourse on AI systems is almost exclusively “Western” in nature.
In other words, ethical deliberations and academic publications are published
by institutions based in Western Europe and the United States and thus imbued
with secular-oriented moral thought. With the expected growth of the AI
industry and the adoption of its technologies by other communities worldwide,
there is a need for diversifying and enriching the current AI moral discourse
by incorporating insights from other cultural and religious traditions.
Available research shows that people’s cultural norms do influence their
understanding of what makes AI systems ethical [347]. Moreover, reports coming
from Muslim-majority countries like Qatar, show that their interest in having
AI technologies is espoused with a parallel interest in developing religio-
culturally sensitive discourse and compliant policies, where also Arabic
language processing will be a national priority [348].

##  8 Conclusions

In this paper, we have reviewed the key challenges in the successful
deployment of AI in smart city applications. In particular, we focused on four
key challenges namely security and robustness, interpretability, and ethical
(data and algorithmic) challenges in the deployment of AI in human-centric
applications. We particularly focused on the connection between these
challenges and discussed how they are linked. Based on our analysis of the
existing literature and experience in the domain, we also identified the
current limitations and the pitfalls of existing solutions proposed for
tackling these challenges. We also identify open research issues in the
domain. We believe such a rigorous analysis of the domain will provide a
baseline for future research.

## Acknowledgment

This publication was made possible by NPRP grant # [13S-0206-200273] from the
Qatar National Research Fund (a member of Qatar Foundation). The statements
made herein are solely the responsibility of the authors.

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