Artificial Intelligence Ethics and Cybersecurity: Overview and

Prospects

Dengxu Li

School of International Education, Quanzhou University of Information Engineering, Quanzhou, Fujian, China

Keywords: Artificial Intelligence, Ethics, Cybersecurity.

Abstract: This paper summarizes the current situation and future outlook in the field of artificial intelligence ethics and

cybersecurity. With the rapid development of AI technology, the industrial upgrading and social progress it

brings cannot be ignored. However, it is accompanied by increasingly severe cybersecurity challenges, such

as information leakage and system crashes, which seriously threaten user privacy and social stability. The

article points out that although AI ethics and cybersecurity have been studied in depth respectively, the

exploration of the intersection of the two is still insufficient. By reviewing relevant research results at home

and abroad, this article emphasizes the importance of AI ethics in ensuring that the use of technology does

not violate human rights and interests and meets ethical standards, and discusses the challenges and solutions

that cybersecurity faces in the era of big data and advanced analytics. The article aims to fill the gap in the

intersection research of AI ethics and cybersecurity, propose countermeasures to deal with the existing

challenges and contribute to building a harmonious and stable social environment.

1 INTRODUCTION

With the rapid development of modern computer

technology, Artificial Intelligence (AI) technology

has become more and more mature and has penetrated

into many fields such as healthcare, education,

finance, etc., which has promoted the progress of

society while upgrading the industry. Artificial

intelligence (AI) technology in OpenAI ChatGPT

application: a review of ChatGPT in writing English

essay mentions that AI technology (such as ChatGPT)

has been developed to facilitate human activities and

productivity. By automating and intelligently

handling tasks, AI can significantly increase

productivity and enable humans to focus on higher-

level work (Fitria, 2023).

However, while AI is becoming more and more

developed, some cybersecurity issues cannot be

ignored, with frequent incidents of information

leakage and website crashes, which greatly threaten

the privacy and security of users, and even pose a

serious threat to social stability. Therefore, while

researching AI, what should not be ignored is the

network security problem, how to strengthen network

maintenance, to ensure that the information is safe

and reliable is a topic that needs to be studied in depth.

AI ethics and network security have always been

two very important topics, which are related to the

stability of social development and the privacy and

security of users. From the current point of view,

although the two topics have been studied more

deeply, the intersection of the two topics has not yet

been insufficient and there are large gaps. This paper

aims to fill the gaps between the two, deeply analyze

and study the impact and challenges of AI ethics and

cybersecurity and put forward countermeasures

against their vulnerabilities, so as to contribute to the

construction of a harmonious and stable society.

Reviewing the research results on AI ethics and

cybersecurity at home and abroad, it is found that

both have been studied in depth. For example,

J.E.(Hans) Korteling and G.C. van de Boer-

Visschedijk discussed how to safely and reasonably

develop AI systems in order to ensure that their use

does not infringe upon the rights and interests of

human beings and at the same time conforms to moral

and ethical standards (Korteling, 2021). Further,

Amin Al Ka'bi in Challenges Encountered by AI

Applications in Today's Interconnected World states

that in the AI/ML field, ethics and transparency are

crucial as they ensure credibility, accountability,

security, and interpretability, which are key to the

widespread adoption and acceptance of these

Li, D.

Artiﬁcial Intelligence Ethics and Cybersecurity: Overview and Prospects.

DOI: 10.5220/0013205200004568

In Proceedings of the 1st International Conference on E-commerce and Artiﬁcial Intelligence (ECAI 2024), pages 13-18

ISBN: 978-989-758-726-9

technologies in today's interconnected world (Al

Ka'bi, 2022). In terms of cybersecurity, Network

security AIOps for online stream data monitoring

demonstrates some of the current issues for

cybersecurity in the field of technology, emphasizing

the dynamic thresholds, deep learning, the role of

SOCs, situational awareness, and the cybersecurity

tools and techniques in addressing modern

cybersecurity challenges (Nguyen, 2022). In

Enhancing cybersecurity protocols in the era of big

data and advanced analytics, it is mentioned that with

the exponential growth of big data, the diversity and

complexity of data is increasing, and the different

formats and sources of data as well as the speed of

data processing require cybersecurity protocols to

have greater adaptability and flexibility (Nwobodo,

2024).

AI ethics are closely related to cybersecurity. In

the future, with the development of technology and

the expansion of application scenarios, people need to

pay more attention to AI ethical issues and actively

search for effective solutions to ensure the healthy

development of AI technology and social well-being.

2 OVERVIEW OF AI

TECHNOLOGY DEPARTMENT

AI is a cross-discipline based on computer science

and technology and cross-fertilized by multiple

disciplines such as computing, philosophy,

psychology, etc. It is a new technological science that

researches and develops theories, methods,

technologies, and application systems used to

simulate, extend, and expand human intelligence. The

research in this field covers a wide range of aspects

such as robotics, language recognition, image

recognition, machine learning, deep learning, natural

language processing and expert systems. AI can

simulate the information process of human

consciousness and thinking, and although it is not

human intelligence, it is able to think like a human

being and may even exceed human intelligence in

some aspects. As discussed in A Scholarly Definition

of AI: Advancing AI as a Conceptual Framework in

Communication Research, the authors define AI as

non-human machines or artificial entities with the

tangible ability to perform, solve tasks, communicate,

interact, and act logically in the real world, as occurs

in biological humans (Gil de Zúñiga, 2024). They

also stated that AI is mainly defined as computational

technology capable of achieving specific tasks,

imitating human behavior, cognitive heuristics and

intelligence.

The Dartmouth Conference held in 1956 marked

the emergence of AI as an independent field of study.

An important branch of early AI was semiotics,

whose main research directions included natural

language processing and logical reasoning, etc. The

1969s and 1970s were the early years of the

development of AI. In 1961, John McCarthy

introduced the term “artificial intelligence”, which

meant that the research on AI was gradually deepened,

but due to the limitations of technological

development and computing power, the development

of AI was relatively slow. 1980s, connectionism and

neural networks became an emerging field of research,

and in 1986, the back propagation algorithm opened

the way to the research and development of neural

networks, and after this, the research direction of AI

gradually shifted to machine learning and deep

learning. 21st century to the present is the beginning

of deep learning. The beginning of the 21st century to

the present is the rise of deep learning, which began

to rise in 2006 with the proposal of deep confidence

networks. Deep learning analyzes and learns the

special features of data and their deeper meanings

through multi-level neurons, and is widely used in

natural language recognition, image recognition,

speech recognition, etc. In 2016, AlphaGo defeated

Go world champion Lee Sedol, demonstrating the

potential of AI and reinforcement learning

technology in the field of AI. This was followed by

the birth of ChatGPT, which was introduced in 2022.

ChatGPT, as a powerful language model developed

by OpenAI, has strong language processing

capabilities and the ability to converse with human

beings, making it a new milestone in AI. The

introduction of ChatGPT marks a major breakthrough

for AI in the field of science and technology, and also

signals that AI will gradually integrate into all aspects

of human life.

The technical classification of AI mainly includes:

machine learning, deep learning, natural language

processing, computer vision, robot vision, robotics,

biometrics, portrait recognition, image recognition,

text recognition, aerospace applications, information

processing and many other types.

An important application of AI in the field of

cyber security is intrusion detection. The system is

able to automatically identify and analyze anomalies

in network traffic through robotic algorithms, thus

enabling timely detection and blocking of virus

intrusions and network attacks. For example, the

malicious code analysis and detection system based

on machine learning can automatically identify and

ECAI 2024 - International Conference on E-commerce and Artiﬁcial Intelligence

extract the characteristics of malicious code and

analyze them, so that network attacks such as Trojans

and worms can be effectively identified.

AI can also predict cyber attacks that have not yet

occurred by analyzing historical data and network

behavior. This predictive ability can help enterprises

and organizations deploy preventive measures in

advance, which can greatly reduce the losses caused

by cyber attacks. Another important task in

cybersecurity is malicious domain name detection.

By introducing data enhancement techniques and

some neural network models, the system is able to

automatically detect malicious domain names, thus

protecting network security.

Currently, AI has infiltrated into people's daily

life as well as all walks of life, and some potential

cybersecurity issues are gradually emerging as AI

becomes more and more prevalent.

The AI technology in vehicles such as

automobile's automatic driving, unmanned vehicles,

drones and other means of transportation, although

not directly related to cybersecurity, the

communication network it uses may become the

target of cyber virus attacks. Once the automatic

driving system is invaded by a virus, it may lead to

serious consequences such as leakage of driving data

and loss of control of the vehicle, seriously

threatening privacy and personal safety. Applications

of AI in financial services include investment

management, risk prediction, stock prediction, and so

on. AI improves the efficiency and accuracy of

financial services. As demonstrated by LONGBING

CAO in AI in Finance: Challenges, Techniques and

Opportunities, AI can assist financial institutions in

risk assessment and monitoring, identifying potential

dangers through complex models, thus dramatically

improving risk management.AI algorithms can

quickly analyze large amounts of transaction data and

identify abnormal transaction behavior, thus

effectively preventing financial crimes (CAO, 2022).

3 EXISTING ISSUES IN

ARTFICIAL INTELLIGENCE

ETHICS

With the rapid development of AI technology, it is

increasingly being used in many areas of life. This has

a positive and positive impact on service quality and

productivity. However, as technology advances, so do

the privacy risks associated with the collection and

use of user data. The development of AI technology,

especially complex algorithms like deep learning,

relies on the training and optimization of large

amounts of data, much of which contains sensitive

information such as location trajectories, behavioral

preferences and personally identifiable information.

The lack of clear guidelines for data use and strict user

permission processes during the data collection phase

may increase the risk of data leakage and misuse.

The risk of data leakage mainly stems from

technical vulnerabilities, human errors or malicious

attacks. CYBERSECURITY RISK mentions cases

such as SolarWinds, in which a cyberattack not only

affected the direct target, but also spread to many

client organizations, including some large U.S.

federal agencies. This suggests that data breaches or

cyberattacks can trigger a chain reaction with indirect

effects on the wider economy (Florackis, 2023).

Data misuse raises more complex ethical issues as

it involves social justice and equity in addition to

privacy violations. Certain companies may use user

data for inappropriate marketing, tailored pushes, and

discriminatory pricing, thus negatively impacting

consumer rights. More importantly, the use of data for

algorithmic decision-making can lead to “algorithmic

discrimination” and exacerbate social inequality if the

training data is insufficient or the algorithm is biased.

For example, gender bias in historical data may lead

employment algorithms to discriminate against

female applicants, while regional disparities may lead

banking algorithms to refuse to serve people in a

particular region.

To solve this problem, a combination of ethical,

technological and legislative elements is necessary.

Legally, relevant laws and regulations must be

updated to strengthen the regulation of data collection,

processing, transmission and storage, and to clarify

the parameters of data use and the responsible parties.

In order to address the difficulties posed by data flows

in the context of globalization, efforts should be made

to harmonize and unify international data protection

legislation. In order to prevent the misuse or leakage

of user data, we should improve data security

protection at the technical level by implementing

encryption technology and using measures such as

anonymous processing. Auditing and traceability

procedures should also be established in order to

ensure the legality and compliance of data use.

Companies should adhere to the values of fairness,

openness and respect when using data, while

reinforcing social responsibility and ethical

obligations. At the same time, they should improve

their personal privacy protection skills and raise

public awareness of data protection, so that they can

work together to create a thriving data ecosystem.

Artiﬁcial Intelligence Ethics and Cybersecurity: Overview and Prospects

In the field of machine learning, although the

algorithms themselves are not subjectively biased,

they may unintentionally reflect social and cultural

biases and discrimination when processing data. This

phenomenon has become one of the hotspots of AI

ethics and fairness research. The bias and

discrimination of machine learning algorithms

mainly stems from the following aspects.

The process of developing machine learning

algorithms begins with data collection and processing,

which usually involves bias aka data discrepancy.

Since training data is usually created by people, social

and cultural biases are inevitable. If historical data

from a hiring system shows gender bias, i.e., the

percentage of successful female applicants is

significantly lower than that of male applicants, then

an algorithm trained on that data may replicate that

bias in hiring decisions, thereby treating female

applicants unfairly. In addition, an important factor

contributing to bias is the underrepresentation of the

dataset. When the dataset is not fully representative

of all groups, the algorithm may not be able to

correctly learn the characteristics of the minority

group, leading to biased decision making.

Next is the design and training of the algorithm.

When dealing with difficult problems, algorithm

designers may unintentionally bias or discriminate,

especially by simplifying assumptions. In addition,

the optimization goal during algorithm training is

usually to maximize some kind of accuracy rather

than to maximize justice. This may cause algorithms

to prioritize high accuracy over specific

demographics. For example, in an image recognition

task, if there are few examples of a certain image type

(e.g., dark-skinned people) in the training dataset, the

algorithm may not be able to correctly learn the

features of that type of image, leading to recognition

bias.

4 AI ETHICS FROM A CYBER

SECURITY PERSPECTIVE

In the field of network security, although the

introduction of AI technology has brought great

innovation to the defense mechanism, it also faces

many difficulties and serious challenges. First, the

endless growth of complex threats is the first

difficulty that AI defense systems must address.

These include ransomware, phishing, and traditional

virus propagation, as well as advanced persistent

threats (apt) and distributed denial of service (DDoS)

attacks, which pose more diverse and subtle threats to

cyberspace security. Traditional defense strategies no

longer seem to be effective in the face of ever-

innovating attacks. While AI systems are able to

detect and react automatically, maintaining effective

and accurate judgment in a complex and dynamic

attack environment has become a major challenge.

In addition, vulnerability exploitation is another

major issue facing AI in cybersecurity. As software

systems become more complex, new vulnerabilities

are often discovered. Attackers can often take

advantage of these weaknesses to launch attacks

quickly. AI systems have the ability to monitor

network traffic and detect abnormal behavior in real

time, but the protection efficiency of AI systems may

be seriously affected by the presence of unknown

security vulnerabilities or new attack techniques.

Therefore, how to improve the defense capability of

AI systems in terms of vulnerability exploitation has

become an urgent problem.

In order to cope with the ethical challenges of AI

in the field of cybersecurity, we need to strengthen the

ethical construction of AI from multiple dimensions.

First, the key to ensure the healthy development of AI

is to improve laws and regulations. In order to control

the use of AI in cybersecurity, clarify the roles and

responsibilities of all parties, and maintain the

progress of AI technology, the government should

formulate clear laws and regulations. Changwu

Huang in An Overview of AI Ethics mentions that

discussing the ethics of AI will inevitably involve the

legal considerations (Huang, 2022). At the same time,

it is necessary to establish a strict regulatory

mechanism to crack down on illegal behaviors and

maintain the security and order of cyberspace.

Second, more regulation is needed to ensure that

AI ethics are properly maintained. As mentioned in

Ethical framework for AI and digital technologies

with the popularization of AI and DT, a series of

ethical issues have surfaced, such as privacy leakage

and algorithmic bias. It is the responsibility of the

government to establish and improve regulatory

mechanisms to ensure that the application of AI meets

ethical standards and prevents potential social risks

(Ashok, 2022).

In addition to establishing joint forces and

collaborative regulatory mechanisms across sectors

and domains, the government should strengthen the

regulation of AI technologies. At the same time,

businesses and non-governmental organizations are

urged to participate in the supervision and utilize their

technology, resources, and other advantages to jointly

promote the application of AI ethics.

In addition, enhancing technology safety is the

cornerstone of safeguarding AI ethics. Security and

ECAI 2024 - International Conference on E-commerce and Artiﬁcial Intelligence

privacy protection need to be considered at the R&D

stage of technology to ensure that AI systems strictly

comply with applicable laws, regulations and ethical

norms when handling sensitive information. At the

same time, technological innovation should be

strengthened to continuously improve the defense and

self-learning capabilities of AI systems in order to

cope with increasingly complex cybersecurity

challenges.

5 CHALLENGES AND PROS-

PECTS

With the rapid development of AI technology, its

application in the field of network security is

increasingly promising. In the future, AI technology

will show the following key development trends in

the field of network security. AI technology will

further enhance the intelligence level of network

security defense, and through machine learning, deep

learning and other algorithms, it will realize the

intelligent identification, warning and response to

network attacks. This trend will greatly improve the

efficiency and accuracy of cybersecurity defense, but

at the same time, it also puts higher requirements on

the accuracy and transparency of the algorithms. As

the technology matures, AI will be able to handle

cybersecurity incidents more autonomously and

automate emergency response. This will shorten the

time from detection to response and reduce human

error, but it may also trigger ethical discussions about

the interpretability of machine decision-making

processes and attribution of responsibility.

Cybersecurity threats are constantly evolving,

requiring AI technologies to have the ability to

continuously learn and evolve. Future cybersecurity

systems will be able to continuously learn new attack

patterns and automatically update defense strategies,

but this self-evolutionary process may also bring new

ethical challenges, such as algorithmic bias and

uncontrollability.

As the application of AI in cybersecurity deepens,

the transparency and interpretability of algorithms

becomes an urgent issue. Users and society need to

understand how algorithms make decisions to ensure

their fairness and reasonableness. Lack of

transparency may lead to public distrust of the

technology and even ethical controversies.

The application of AI in cybersecurity inevitably

involves the collection, processing and analysis of

large amounts of data. How to protect personal

privacy and data security has become an important

ethical issue. Strict data protection mechanisms need

to be established to ensure the security and privacy of

data during collection, storage, transmission and use.

In automated emergency response and intelligent

defense systems, it becomes difficult to determine the

attribution of responsibility when the system makes

mistakes or causes problems. Clear accountability

mechanisms need to be established to ensure that

those responsible can be held accountable when

problems arise, while protecting technological

innovation and development.

Given the potential impact of the ethical issues

mentioned above, it is particularly important to

establish a comprehensive and systematic code of

ethics for AI. This will not only help regulate the

R&D and application behavior of AI technology and

reduce ethical risks, but also help enhance public trust

and acceptance of the technology.

In order to promote the construction of AI ethical

norms, the construction of international cooperation

mechanisms is crucial. Different countries and

regions have different views and positions on AI

ethics, and through international cooperation, mutual

understanding and consensus can be enhanced, and

ethical norms and standards of universal applicability

can be jointly formulated. At the same time,

international cooperation can also promote the

exchange and sharing of technology and promote the

healthy development of AI technology.

Therefore, this paper suggests strengthening

international dialog and cooperation to jointly

promote the construction of ethical norms for AI.

Through the formulation of clear ethical principles,

the establishment of an effective regulatory

mechanism, and the strengthening of ethical review

in the research, development and application of

technology, we can ensure that the research,

development and application of AI technology meets

ethical requirements and promotes the sustainable

development of society.

6 CONCLUSIONS

This paper focuses on the ethical and cybersecurity

issues facing the rapid development of AI technology.

The paper first outlines the application of AI

technology in the fields of healthcare, education,

finance, etc. and its positive impact on the society,

and then points out the threats to user privacy and

security and social stability posed by cybersecurity

issues such as information leakage and website

crashes. Then, the paper deeply analyzes the close

connection between AI ethics and cybersecurity, and

Artiﬁcial Intelligence Ethics and Cybersecurity: Overview and Prospects

reviews domestic and international research results in

this field, such as the ethical discussion on the

development of AI systems and the importance of

ethics and transparency in the AI/ML field. In

addition, the paper introduces some current key issues

and technical solutions in the field of cybersecurity,

such as the application of machine learning and deep

learning in cybersecurity monitoring. Finally, the

paper looks into the future direction of AI ethics and

cybersecurity, emphasizing the importance of paying

attention to ethical issues and actively finding

solutions to ensure the healthy development of AI

technology and social stability.

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