Explainability-Based Artificial Intelligence Governance: A Legal

Framework Analysis of Ethical and Social Impacts

Bulut Keken

and Cemal Aktürk

Gaziantep Islamic Science and Technology University, Gaziantep, Turkey

Keywords: Artificial Intelligence, Ethical Rules, Social Impacts, Legal Regulations, Explainability, Ethical Design.

Abstract: Even though artificial intelligence (AI) systems, also known as "thinking machines," have become an

indisputable aspect of technological advancement in society, it is impossible to ignore the moral dilemmas

and societal effects they raise. These difficulties necessitate the introduction of legal regulations as well as

the precise definition of AI's standing, obligations, limitations, and accessibility. Legislators and policymakers

have therefore begun to assess AI advancements in the context of moral ideals and principles, particularly in

reaction to particular applications that have sparked public outrage. With an emphasis on the lack of

explainability in AI systems, this study examines these problems and draws attention to specific legal

loopholes. Countries and international organizations are currently developing ethical standards for artificial

intelligence. These guidelines are frequently disregarded, though, when developing software, defining

developer accountability, and describing the decision-making process of AI. Therefore, to demonstrate how

ethical and legal considerations can be incorporated into the technical architecture of AI systems, a

governance framework centred on explainability, accountability, and ethical design is required.

1 INTRODUCTION

1.1 Artificial Intelligence

With the ultimate goal of enhancing human life,

artificial intelligence (AI) is a technology that can

complete complicated tasks faster and more

effectively than humans. The advancement of AI has

primarily been driven by the desire to benefit

humanity. AI tools have become increasingly popular

since the advent of large language models like

ChatGPT and Google Bard. These days, digital tools

that produce text, images, audio, and video are

frequently thought of when AI is mentioned. Over the

past 20 years, the applications of AI have rapidly

expanded into almost every field, despite the fact that

its full scope is still not fully understood(Kurtuluş,

2023).

Numerous industries, including healthcare,

manufacturing, transportation, security, education,

and social life, are now utilizing AI applications,

which have significantly changed the world. In the

upcoming years, their range of applications is

https://orcid.org/0009-0009-0656-1144

https://orcid.org/0000-0003-3764-3862

anticipated to grow even more. AI algorithms raise

significant ethical issues as they play a bigger and

bigger part in the digitalized facets of human life and

integrate into social structures(Yeşilkaya, 2022).

Concern is growing throughout society over the

possible drawbacks of AI in fields that require face-to-

face communication(Ashraf, 2022). The most

important applications of AI are the main focus of this

study. Additionally, it emphasizes the significance of

social and ethical oversight, highlighting the fact that

the efficacy of legal regulations is influenced by both

normative principles and the ways in which these

principles are incorporated into technological systems.

Although many ethical principles have been

proposed in recent years, their integration into the

software development process, the clarification of

developer responsibilities, and the explainability of AI

decisions remain largely overlooked. Particularly in

intricate, deep learning-based systems, this ambiguity

breeds uncertainty and endangers people's rights.

The goal of this study's next phase is to suggest a

governance framework based on accountability,

explainability (XAI), and ethical-by-design

Keken, B. and Aktürk, C.

Explainability-Based Artiﬁcial Intelligence Governance: A Legal Framework Analysis of Ethical and Social Impacts.

DOI: 10.5220/0014364700004848

Paper published under CC license (CC BY-NC-ND 4.0)

In Proceedings of the 2nd International Conference on Advances in Electrical, Electronics, Energy, and Computer Sciences (ICEEECS 2025), pages 287-294

ISBN: 978-989-758-783-2

287

methodologies. This framework aims to provide a

comprehensive model that assigns responsibilities not

only to developers but also to regulatory bodies, user

communities, and ethics committees by combining

technical elements and normative principles. By doing

this, the study hopes to investigate how legal and

ethical frameworks can be converted into technical

architectures, especially when explainable AI and

ethical-by-design approaches are used.

1.2 Explainable Artificial Intelligence

Making AI systems' decisions and outputs

comprehensible and interpretable for humans is the

goal of the research field known as Explainable

Artificial Intelligence (XAI)

(Adadi and Berrada, 2018).

XAI is sometimes referred to as "AI for humans," with

the goal of bridging the gap between human

comprehension and machine intelligence by assisting

users in understanding the reasoning behind decisions

and their reliability(Angelov et al., 2021). These

initiatives are mostly the result of growing concerns

about the trustworthiness and transparency of AI. The

need for new AI techniques that make AI decisions

more understandable and explicable is being driven

more and more by social, ethical, and legal

pressures(Adadi and Berrada, 2018).

1.3 Artificial Intelligence Ethics

The moral standards and guidelines that direct the

conduct of individuals or groups and aid in

determining what is right and good are referred to as

ethics. According to this definition, AI ethics are the

rules and values that influence how AI systems

behave(Turan et al., 2022). Ethical AI highlights how

crucial ethical factors are in determining what

applications of AI are acceptable and unacceptable

(Yeşilkaya, 2022).

2 SOCIAL AND ETHICAL

PROBLEMS OF ARTIFICIAL

INTELLIGENCE

2.1 Ethical and Social Impacts in

Application Areas

2.1.1 Ethical and Social Impacts in the

Health Sector

AI is known to speed up diagnostic procedures by

analysing data and recognizing images. It is important

for drug development, robotic surgery, patient

monitoring, and early disease detection. Due to

benefits like three-dimensional imaging, the removal

of hand tremors, better access to organs, tissues, and

nerves, and the ability to provide surgeons with an

ergonomic range of motion, robots are becoming

more and more popular in surgical procedures for a

variety of diseases(Walters and Eley, 2011). It is clear

that AI offers humanity enormous advantages and

conveniences due to its creativity, speed, and

performance in healthcare. Nonetheless, healthcare

providers now have continuous access to patient

records thanks to advancements in electronic record

systems. This circumstance highlights the need for

legal regulations to protect patient data within

healthcare automation and presents ethical questions

regarding patient privacy and

confidentiality(Özdemir and Bilgin, 2021). In this

case, deciding who will be responsible for any

problems resulting from AI-performed diagnoses or

surgical procedures and how to handle the fallout are

the most important ethical and social issues. "AI

systems should only assist clinical decision-making

processes, with ultimate responsibility resting on

physicians as part of their clinical duties," the World

Health Organization (WHO) emphasizes in its six

basic principles regarding responsibilities in AI

applications. Making wise clinical decisions and

guaranteeing patient safety depend heavily on this

strategy"(World Health Organisation, 2024).

2.1.2 Ethical and Social Impacts of

Autonomous Vehicles

The creation of autonomous vehicles is among the

most noteworthy developments in artificial

intelligence. These cars use onboard cameras and

sensors to sense their environment and navigate on

their own. This feature is an obvious illustration of

AI's potential to improve comfort and benefit society

since it allows independent mobility for the elderly,

disabled, and those who are unable to drive. There are

many other benefits as well. However, in addition to

the advantages for society, the use of autonomous

vehicles also brings up moral and security issues. For

example, who is responsible for an accident—the

consumer, the developer, or the manufacturer? This

problem was attempted to be resolved by researchers

at the Technical University of Munich in their paper

"An Ethical Trajectory Planning Algorithm for

Autonomous Vehicles." Maximilian Geisslinger, one

of the authors, clarified: "Up until now, autonomous

vehicles (AVs) have always had to make a moral

decision. But traffic conditions in the real world are

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rarely straightforward. Our algorithm makes an

ethical choice in a matter of seconds after weighing

thousands of potential courses of action and

evaluating various risks (Geisslinger et al., 2023).

This problem is especially significant because, in a

recent instance, Mercedes-Benz used an algorithm

created for its autonomous cars that put the safety of

its passengers before that of other people. This

strategy presents a significant ethical question

regarding how other drivers' and pedestrians' safety is

taken into account in emergency situations.

2.1.3 Ethical and Social Impacts of

Recruitment Processes

Recruitment procedures are another area where AI's

ethical limitations are especially noticeable. AI can

improve the effectiveness of planning, hiring, and

candidate evaluation, allowing businesses to make

decisions more swiftly and efficiently. But there are

also serious moral and societal repercussions to this.

Although AI may at first appear to help employers

make decisions more quickly, there are worries that

discrimination may result from biased data used in

hiring criteria. Specifically, candidates may be

unfairly disadvantaged by AI systems on the basis of

personality traits, gender, or race(Chen, 2023).

Historical data is used to train AI algorithms. The AI

may use patterns learned from previous hiring

practices to unjustly exclude some candidates from

the selection process if this data contains

demographic biases or if people with disabilities are

not like the majority. This could compromise

diversity.

2.2 Ethical and Social Impacts in the

Field of Personal Rights and

Freedoms

2.2.1 Data Privacy and Security

As sophisticated data discovery methods have

become more prevalent, privacy has become a

significant social issue. Individuals can now be easily

identified, profiled, and influenced without their

knowledge or consent. These processes are speeding

up as AI systems develop, which raises more privacy

concerns(Eryılmaz, 2023).

2.2.2 Discrimination and Prejudices

The datasets used for training have a significant

impact on how ethical and social values are integrated

into AI systems. AI systems will pick up on and

reinforce human biases if they are present in these

datasets. In particular, biases may be strengthened if

training data contains discriminatory information

about particular groups, religions, or physical

characteristics. Religious beliefs are a significant

issue that could result in prejudice and discrimination

in AI algorithms, their outputs, and the broader legal

frameworks. It has long been known that human

beliefs have influenced societies since the beginning

of time, and people have structured their lives around

these beliefs(Demir, 2024).

2.2.3 Trust and Transparency in

Human-Machine Interaction

One of the key issues discussed within AI ethics is the

ethical coding of robots designed for critical

missions, such as robot police and robot soldiers.

Otherwise, robot systems with military functions

could lead to serious ethical violations(Karabağ,

2021).

2.2.4 Working Life and Unemployment Risk

There is a need for political and legal regulations that

will minimize the unemployment problem that will

arise from discrimination and technology, and to

experience the negative effects of unemployment in a

more reasonable way(Doruköz and Uslu, 2023). The

material and moral devastation that people would

experience due to the fear of unemployment during

the rapid transitions prior to these policies could lead

to unrest and anxiety within society. A study

conducted in both developed and developing

countries found a non-linear relationship between AI

and unemployment, depending on the inflation

threshold. In other words, AI increases

unemployment until a certain inflation threshold is

reached, after which its impact diminishes.”(Nguyen

and Vo, 2022). Of course, this process still hasn't

prevented unemployment in some sectors, and this

has negatively impacted people's perspective on

technological advancements. This continues to be a

social problem

3 LEGAL REGULATIONS

3.1 Current Legal Gaps and Risks

The rapid development of AI technologies has created

a situation where existing legal frameworks are

inadequate to adapt to these innovations(Kara

Kiliçarslan, 2019). Due to developing technology and

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new situations, different communities and beliefs,

and lifestyles, common and appropriate legal

decisions cannot be made for every situation. This

situation has created some legal gaps and risks.

Views on the legal status of AI are gathered

around the issue of whether AI should be positioned

as an object, thing or product, or as a non-human

subject. The view that evaluates AI as an object

argues that the rights and responsibilities of AI can

develop in a very limited way and that this can be

regulated by an insurance system.”(Perennou, 2019).

While it is obvious that AI is coded by humans,

the first idea that makes sense is that it is considered

as a thing. For this reason, it is the most reasonable

legally to see it as a thing. It should be accepted that

insurance companies can be intermediaries in

allocating the damages that AI may cause, therefore

it cannot be a subject of rights, but it should be

accepted as an object that can be defined by

ownership. (Akkurt, 2019).

The concept of electronic personhood was

proposed in the Recommendation on Civil Law Rules

on Robotics (27 January 2017) prepared by the

European Parliament (EP) Committee on Legal

Affairs, as a possible solution to some fundamental

issues in the fields of robotics and law. The electronic

person concept is considered more appropriate than

the object concept when considering the autonomous

characteristics of AI(Yenice, 2024).

While there is no clear consensus on its legal

status yet, it is still unclear who the law should punish

and hold responsible in which areas and how. For

example, if an autonomous vehicle causes an

accident, how will it determine who will be

responsible? Who will be responsible if a robotic

device used in a surgery causes the death of a patient?

In addition, the decision-making processes of AI

systems are referred to as a "black box"(Öztemel,

2012). The lack of transparency in these systems

makes legal oversight and accountability difficult.

This is because the term "Black Box" refers to the

lack of transparency and accountability in the data

used by AI and human observers, or in the decision-

making processes. In other words, "Black Box" AI

systems refer to AI systems that are primarily opaque

neural networks, whose inputs and operations are

invisible to neither the user nor other interested

parties(MacCarthy, 2020). For this very reason, the

XAI initiative demonstrates the ability to explain the

decision-making processes underlying such large and

complex systems in terms and formats

understandable to experts in the field(Angelov et al.,

2021).

4 INTERNATIONAL LEGAL

APPROACHES

In order to find solutions to the ethical, social and

security problems of AI systems, international

organizations such as the OECD and the European

Parliament have considered that a set of rules and

frameworks should be determined and have included

comprehensive regulations that will make AI more

problem-free in terms of ethical and social effects and

protect human life from negative effects(Güner,

2019).

The negative impacts of AI use on human rights

have led to increased concerns in this area at national

and international levels. Accordingly, in the “Guide

to Ethical Principles for Trustworthy Artificial

Intelligence Systems” published by the Council of

Europe on December 18, 2018, which guides AI

designs based on human rights, it is seen that various

requirements aimed at addressing these concerns are

addressed. These requirements are; “maintaining

basic human rights, technical robustness and security,

which are closely related to the principle of

prevention of harm, and privacy (privacy of private

life) and data management, which are closely related

to the principle of explainability, transparency, which

are closely related to the principle of fair treatment,

diversity, non-discrimination and fairness, which are

closely related to the principle of accountability and

fair treatment, and ensuring social well-being and

protecting the environment, which are closely related

to the principle of prevention of harm.”(Singil, 2022).

4.1 OECD AI Principles

The OECD Principles on Artificial Intelligence

support AI to be innovative, trustworthy and ethical.

These principles were adopted by OECD member

countries on May 22, 2019, and are among the first

global principles on AI to be signed by governments.

Non-OECD countries such as Argentina, Brazil,

Colombia, Costa Rica, Peru and Romania also adhere

to these principles(OECD, 2024).

Although OECD recommendations are not legally

binding, the framework they created and the decisions

taken have become the basis of international

standards and governments have prepared their own

legislation within this framework(Güner, 2019).

It states that AI systems should be beneficial to

society and support inclusive growth and sustainable

development. It also emphasizes that AI should be

developed in a way that respects the rule of law and

human personal rights, always keeping the

transparency criterion at the forefront and ensuring its

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security, that is, protection against attacks, during its

operation. Finally, it is stated that institutions and

organizations that develop or operate AI systems

should act in accordance with these

principles(OECD, 2024).

4.2 European Parliament AI

Regulations

The “Proposal for a Regulation Amending Certain

Union Legislative Acts Providing Harmonized Rules

on AI” was adopted by the European Union (EU)

Commission on 21 April 2021, with the high

participation and approval of the European

Parliament (EP), which sets out the limitations and

prohibitions to be observed for AI systems made

available to humans(European Union, 2022a).

With this regulation, the EU prohibits attempts by

AI to violate fundamental rights and equality

principles of society, such as security and privacy, in

matters such as discrimination, economic rights or

behavioral guidance caused by biometric

classification that affects people ethically and

socially(European Union, 2021b). The draft

regulation prepared by the European Commission

was followed by the opinion adopted and published

by the Council of the European Union (“EU”) on the

general approach to the regulation in December

2022(European Union, 2022a). In June 2023, the

draft study prepared by the Members of the European

Parliament regarding the approach to amend the

Commission's draft regulation was published

(European Parliament, 2024).

5 LEGAL APPROACHES AND

CURRENT DEVELOPMENTS

ABOUT AI IN TÜRKİYE AND

THE WORLD

5.1 Current AI Regulations and

Developments in Türkiye

Although there is no direct legal regulation regarding

AI technologies in Türkiye, some provisions related

to these technologies have been added with some

regulations made in the legislation with the Personal

Data Protection Law (KVKK). Although biometric

data in particular is not clearly defined within the

scope of KVKK, it is stated in the "Guide on the

Processing of Biometric Data" published by KVKK

that the General Data Protection Regulation (GDPR),

which is also referenced in the European Union

Artificial Intelligence Law, provides the most

comprehensive definition.

Due to the use of personal data in AI systems,

KVKK also aimed to draw attention to the relevant

issue by publishing a document titled

“Recommendations on the Protection of Personal

Data in the Field of AI”. Under the coordination of

the Presidency’s Digital Transformation Office,

studies on eliminating AI risks and ethical practices

continue with the contributions of different

stakeholders, primarily the Ministry of Industry and

Technology, TÜBİTAK Bilgem, AI Institute and

Turkish Standards Institute. The AI Risk

Management Framework has been implemented and

a reference plan has been provided for mapping,

measuring and managing risks. An inventory and

needs pool has been created for AI and advanced

analytics projects carried out in the public sector. The

Public Data Area project is being implemented in

cooperation with TÜİK. Public stakeholders can

store, share and process data in accordance with

reliable, advanced standards and guidelines in a way

that guarantees data sovereignty(TRAI, 2017).

5.2 Other Legal Regulations and

Decisions Implemented Worldwide

5.2.1 Global Partnership on Artificial

Intelligence

The AI Global Partnership (GPAI) consists of 29

members, including Türkiye, the United Kingdom

and the European Union. Its aim is to fill gaps and

deficiencies in the theoretical and practical fields of

AI(OECD.AI, 2019).

5.2.2 Meeting of the United Nations Security

Council

18 At the council's first official meeting on 18 July

2023, United Nations Secretary-General António

Guterres, speaking on AI and generative AI,

emphasized the need to ‘work together for AI that

bridges social, digital and economic divides’ He also

noted that governments could establish robust and

reliable evaluation systems to hold companies

accountable, thereby enabling them to gain global

trust(OECD.AI, 2019).

5.2.3 G20 AI Principles

The G20 AI principles, which are based on OECD

principles, were adopted in 2019. Their aim is to

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address concerns about AI and increase trust in AI

with a human-centered approach. These principles are

not binding, as in the OECD principles.(OECD.AI,

2019).

5.2.4 AI Safety Summit and Bletchley

Declaration

The Bletchley Declaration, which was accepted as the

first summit where country representatives and

companies came together on November 1-2, 2023, to

recognize the potential of AI to increase human well-

being and peace, was accepted by 28 countries,

including Türkiye. Its aim is to ensure that AI is

designed, developed and implemented in a reliable,

responsible and human-focused manner(AI Safety

Summit, 2023).

5.2.5 The World Economic Forum AI

Governance Alliance

The AI Governance Alliance was established in June

2023 under the umbrella of the World Economic

Forum, bringing together industry leaders,

government officials, academics and civil society

organizations. The alliance aims to provide guidance

for the ethical, responsible and secure design,

development and deployment of AI systems, and has

more than 250 members.

In addition, AI was among the main topics at the

54th World Economic Forum held in 2024. OpenAI

CEO Sam Altman emphasized that AI offers more

advanced tools and capabilities to humans, and

expressed the need for coordinated governance for

this technology to benefit in an inclusive and ethical

way(Harvey, 2024).

5.2.6 USA Executive Order 13859

With the Presidential Decree titled Maintaining

American Leadership in AI dated February 11, 2019,

the US declared that it will continue to be a pioneer

in AI and that it is bold in using AI technologies,

emphasizing that it will ensure people's security,

personal freedoms and privacy, and that it will stand

against all obstacles, including the Office of

Management and Budget, to benefit from all the

possibilities of AI in order to protect American

values. (Federal Register, 2019).

Guidance for Regulation of Artificial Intelligence

Applications. The Office of Management and Budget

published the Guidance for Regulating Artificial

Intelligence Applications on November 17, 2020. The

guidance lists the following key elements to consider

when regulating AI applications: ensuring public

trust, public participation in the process, scientific

integrity and accuracy of information, assessing and

managing risks, cost-benefit analysis, flexibility,

preserving justice and preventing discrimination, the

principle of information and transparency, security

measures, and strengthening interagency

cooperation(MacCarthy, 2020).

Voluntary Commitments of Technology

Companies. Beyaz On July 21, 2023, the White

House announced that OpenAI, Amazon, Anthropic,

Google, Inflection, Meta, and Microsoft have

voluntarily committed to taking measures to make AI

technologies safer and protect users. These measures

include investing in cybersecurity, conducting

research on discrimination, and developing new

watermarking systems that will notify users of AI-

generated content. Additionally, on September 12,

2023, the White House announced that eight other

technology companies, including Adobe, IBM, and

Salesforce, have made similar voluntary

commitments(Harvey, 2024).

Executive Order on Safe, Secure, and

Trustworthy Artificial Intelligence. In order to

ensure the safety of AI with the new standards, the

Presidential Decree on Safe and Reliable AI was

published on October 30, 2023. (Harvey, 2024).

6 DISCUSSION

All facets of social life have been impacted by

artificial intelligence (AI) systems, and it is generally

accepted that careful technical, ethical, and legal

assessments are required(Floridi and Cowls, 2019).

This need is attested to by the legal actions taken by

nations and organizations around the world. The

opaque (black box) structures involved in AI

decision-making are frequently difficult for users,

developers, and decision-makers to comprehend. As

a result, these systems lose their transparency and the

lines separating accountability become increasingly

hazy(Floridi and Cowls, 2019). For example, the

assignment of ethical and legal responsibility is

complicated when autonomous vehicles are unable to

provide an explanation for "why a particular decision

was made" in the event of an accident(Awad et al.,

2018). Similarly, when algorithmic systems make

decisions that have a direct impact on people's lives,

like hiring, medical procedures, or credit scoring,

unexplained results raise the possibility of

discrimination(Wachter et al., 2017).

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Explainable Artificial Intelligence (XAI) is

essential for ethical responsibility and legal

monitoring in this regard. Explainability, however,

shouldn't be limited to just making a model's technical

elements visible. Technical transparency and giving

users easily accessible and intelligible explanations

are two very different things(Wachter et al., 2017).

Even after all model parameters are disclosed, the

user may not always fully grasp the logic behind a

decision(Mittelstadt et al., 2019). Ananny and

Crawford (2018)(Ananny and Crawford, 2018)

specifically draw attention to the shortcomings of the

ideal of transparency, contending that accountability

and openness are not always interchangeable.

Consequently, explainability is not just a technical

necessity but rather a sociotechnical obligation.

It's now clear that ethical considerations must be

incorporated into AI system design. This method

encourages system developers to accept internal

ethical responsibility while also offering external

oversight.(Hagendorff, 2020). In this context, IEEE’s

Ethical Design (2019) (IEEE.org, 2019) report

emphasizes that integrated design at the design level

is necessary for ethical governance to be effective in

artificial intelligence systems.

Therefore, this study looked at the intersection of

technical, ethical, and legal requirements within the

explainability framework and discovered that

systems that cannot be explained can pose serious

problems for social acceptance and accountability.

7 CONCLUSION

In particular, this study looked at how XAI techniques

can satisfy the structural need for explainability in AI

systems in morally and legally challenging situations.

Explainability is the ability to keep an eye on

decisions, make sure they are understandable, and

permit critical assessment(Floridi and Cowls, 2019).

But it's crucial to understand that explainability is a

sociotechnical obligation as well as a technical

requirement. To facilitate equitable and

understandable technological decisions, explanations

should be in line with users' mental models;

meaningful user interactions—rather than merely

internal system documentation—are crucial to

promoting transparency(Mittelstadt et al., 2019). In

this context, systems created using ethical-design

methodology open the door to both internal corporate

responsibility and external audit(Dignum, 2019). It is

suggested that explainability mechanisms be

redesigned in order to create AI systems that are

human-centered and sensitive to social contexts.

Finally, “explainability serves the dual purposes

of ensuring that technological decisions are both

comprehensible and socially acceptable."

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