Review Articles Open Access

Ethical Implications of Artificial Intelligence: A Review, Taxonomy, Governance Framework, and Future Research Agenda

Soban Ahmad , Zupash Awais
Available online January 1, 2025
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Abstract

Artifical Intelligence (AI) is used in routine consumer services as well as in systems that support institutional decisions. Health care, education, business, transport, communication, robotics, and public services are some core applications of AI. These applications create ethical questions because they may process personal information or influence decisions that affect people. The discussion therefore includes privacy, fairness, accountability, security, human control, and changes in employment. In this paper, we starts from a keyword-based Systematic Mapping Study (SMS). We searched different databases like IEEE Xplore, ACM Digital Library, Scopus, Web of Science, and ProQuest. In result of searching 1,062 papers returned. After that screening reduced the set to 83 academic papers, and 37 recurring keywords were reported. The keywords were classified into nine categories covering conceptual research, robotics, general philosophical and ethical research, AI-specific philosophical and ethical research, law and regulation, autonomous vehicles, artificial general intelligence and AI risk, human cognition, and technology-oriented research. Selected papers published from 2021 to 2024 are therefore used to update the discussion on trustworthy AI, generative AI, risk management, security, and regulation. Top-down and bottom-up machine ethics, digital policy, data governance, and ethical questions arising during AI development and operation are also reviewed.

Keywords

Artificial Intelligence Ethics Trustworthy AI AI Governance Generative AI Systematic Mapping Study

Introduction

AI methods perform tasks such as classification, prediction, speech and image recognition, recommendation, planning, and decision support [1]-[3]. Their role depends on the type of application. A model may work in the background, provide information to a user, or contribute to the control of a physical system. These forms of AI are found in online services, health care, education, transport, communication networks, and industry. Accuracy alone does not describe the effect of an AI application on the people who use it or are affected by it. Personal information may be required as input, and the resulting output may influence a decision about a person or group. Questions then arise about the source of the data, unequal treatment, explanation of the result, and responsibility for its use [4]-[6]. Problems like fairness, privacy, transparency, accountability, safety, human control, and responsibility are discussed in the AI-ethics literature. Several of these problems originate in normal development decisions. Training data determine from which cases and groups the model learns. The interface determines what users are told about an automated result. Testing can also reveal differences in error across groups. Where an incorrect result has serious consequences, human review may be required before the output is acted upon. Responsible-AI research considers these questions during design, testing, and deployment [7], [8]. Law introduces requirements that are different from voluntary ethical principles. Robles Carrillo [9] examines the relationship between AI ethics and legal regulation. Pagallo et al. [10] discuss governance, data protection, and AI. Their work covers issues such as liability, permitted data use, and restrictions placed on particular applications. Renda [11] considers AI within the wider setting of digital policy and governance. International guidance provides another source of requirements. UNESCO’s Recommendation on the Ethics of Artificial Intelligence [12] covers human rights, fairness, transparency, human oversight, and governance. NIST [13] approaches AI from the perspective of risk management. The European Union Artificial Intelligence Act [14] provides legal requirements that vary according to the type and use of the AI system. Vakkuri and Abrahamsson [15] studied the earlier AI-ethics literature using a keyword-based SMS. The initial result contained 1,062 papers. After screening, Following screening, 83 academic papers were retained for the keyword analysis. From these papers, 37 recurring keywords were identified. The 37 keywords were placed into nine categories: conceptual research, robotics, general philosophical and ethical research, AI-specific philosophical and ethical research, law and regulation, autonomous vehicles, artificial general intelligence and AI risk, human cognition, and technology-oriented research [15]. The present paper uses these nine categories as the main structure for reviewing the earlier AI-ethics literature. The SMS was published in 2018. AI systems have changed considerably since then, particularly with the wider use of large language models and generative AI. These systems have brought greater attention to training data, generated content, misuse, large-scale deployment, and the difficulty of controlling model behavior. Research after the original mapping has examined harm at different stages of the machine-learning lifecycle [16]. Large language models have been studied in relation to training data, representation, misuse, and other risks [17], [18]. Trustworthy-AI research has also developed further [19]. Studies published after the introduction of ChatGPT examine the use and implications of generative AI in research, education, and other settings [20]-[22]. Recent work has also considered the relationship between trustworthiness and regulation [23] and the security, privacy, and robustness of trustworthy AI systems [24]. The NIST framework [13] and the EU Artificial Intelligence Act [14] form part of the same recent governance discussion. This paper uses the earlier nine-category taxonomy as its starting point and then considers how the discussion has changed in more recent work. The first part reviews the ethical concepts reported in the SMS. Selected literature from 2021 to 2024 is then used to discuss developments related to large language models, generative AI, trustworthy AI, security, risk management, and regulation. The newer studies are treated as an update and are not included in the original SMS counts. The later publications are treated as an update and are not included in the original figures of 1,062 retrieved papers, 83 retained papers, or 37 recurring keywords.

The remainder of the paper is organized as follows. Section 2 describes the systematic mapping method and the literature covered in this paper. Section 3 introduces the AI application areas needed for the later discussion. Section 3 reviews previous work on AI ethics, responsible design, law, and governance. The nine-category taxonomy is discussed in Section 5. Section 6 examines the main ethical issues, while Section 7 discusses top-down and bottom-up approaches to machine ethics. Digital policy and data governance are covered in Section 8. Section 9 discusses selected developments reported between 2021 and 2024. Section 10 considers ethical issues during AI-system development and deployment. Sections 11 and 12 present the discussion and limitations, Section 13 identifies future research directions, and Section 14 concludes the paper.

Complete Article

The complete article, including all figures, tables, equations and algorithms, is available in the official publication PDF.

Conclusion

AI is now used in systems that support everyday services as well as decisions in health care, education, industry, communication, and public administration. The technology can reduce manual work and process information at a scale that is difficult for people, but model performance answers only part of the question. The way data are collected, the way a result is used, and the responsibility attached to that result remain important. The SMS reported in [15] retrieved 1,062 papers, retained 83 academic papers, and identified 37 recurring keywords organized into nine categories. The nine categories cover conceptual, philosophical, legal, human-centered, application-specific, and technical aspects of AI ethics. They offer a standard method for classifying issues that arise in various AI applications. Throughout the system lifespan, ethical concerns must also be taken into consideration. Even if a more powerful method is subsequently used, an issue that was established during the task definition or dataset preparation may still exist. Monitoring, deployment, testing, and implementation can all cause new issues. When setting organizational, safety, or legal boundaries, top-down regulations are helpful. When the system must adjust to circumstances that are not fully predetermined, bottom-up learning can be applied. The individuals and institutions who create, authorize, and utilize the deployed system are still in charge of it [7], [83]. The prior subject is expanded upon by the material released between 2021 and 2024. Large language models, trustworthy-AI frameworks, generative AI, lifecycle hazards, risk management, and current regulations are all included [12]–[14], [16]–[24]. The review is updated using these subsequent research. Neither the initial SMS numbers nor the counts stated in [15] are altered by them. Reproducible reviews, quantifiable ethical standards, data governance, human supervision, security, and domain-specific evaluation are all strengthened by them.

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