Is Blockchain the Future of AI Alignment? Developing a Framework and a Research Agenda Based on a Systematic Literature Review

Abstract

Artificial intelligence (AI) agents are increasingly shaping vital sectors of society, including healthcare, education, supply chains, and finance. As their influence grows, AI alignment research plays a pivotal role in ensuring these systems are trustworthy, transparent, and aligned with human values. Leveraging blockchain technology, proven over the past decade in enabling transparent, tamper-resistant distributed systems, offers significant potential to strengthen AI alignment. However, despite its potential, the current AI alignment literature has yet to systematically explore the effectiveness of blockchain in facilitating secure and ethical behavior in AI agents. While existing systematic literature reviews (SLRs) in AI alignment address various aspects of AI safety and AI alignment, this SLR specifically examines the gap at the intersection of AI alignment, blockchain, and ethics. To address this gap, this SLR explores how blockchain technology can overcome the limitations of existing AI alignment approaches. We searched for studies containing keywords from AI, blockchain, and ethics domains in the Scopus database, identifying 7110 initial records on 28 May 2024. We excluded studies which did not answer our research questions and did not discuss the thematic intersection between AI, blockchain, and ethics to a sufficient extent. The quality of the selected studies was assessed on the basis of their methodology, clarity, completeness, and transparency, resulting in a final number of 46 included studies, the majority of which were journal articles. Results were synthesized through quantitative topic analysis and qualitative analysis to identify key themes and patterns. The contributions of this paper include the following: (i) presentation of the results of an SLR conducted to identify, extract, evaluate, and synthesize studies on the symbiosis of AI alignment, blockchain, and ethics; (ii) summary and categorization of the existing benefits and challenges in incorporating blockchain for AI alignment within the context of ethics; (iii) development of a framework that will facilitate new research activities; and (iv) establishment of the state of evidence with in-depth assessment. The proposed blockchain-based AI alignment framework in this study demonstrates that integrating blockchain with AI alignment can substantially enhance robustness, promote public trust, and facilitate ethical compliance in AI systems.

1. Introduction

The rapid development of artificial intelligence (AI) has made it a foundational pillar of innovation in many important sectors such as healthcare [1], finance [2], education [3], and transportation [4]. As AI systems advance, with increasingly efficient underlying algorithms and computational power, their impact on society deepens, presenting a wide range of ethical challenges and potential risks [5]. These challenges pertain to the ethical governance of AI systems, the extent of human oversight, and ensuring that these systems adhere to ethical standards [6]. The potential for AI systems to behave unpredictably and in ways that may contradict human values is a growing concern, especially when these systems are designed to evolve or operate autonomously [7]. Furthermore, scenarios where AI agents compete for limited resources or an AI arms race could escalate tensions and further exacerbate these risks [8].

To address these challenges, the concept of AI alignment has emerged as a significant area of research. AI alignment refers to the process of developing AI systems that not only perform their intended tasks effectively, but also operate in a way that is compatible with human values and ethical principles [6,9]. Robust AI alignment is essential to mitigate the risks of complex autonomous AI systems, yet the field faces significant limitations [9]. AI alignment techniques are still in their early stage of development and often lack the robustness required to effectively address the full range of potential risks [9]. Current approaches cannot keep up with the rapidly evolving AI landscape, highlighting the need for better ethical oversight and improved governance of these systems [10].

Alongside these developments, blockchain has emerged as another transformative technology that is attracting increasing attention for its potential role in AI alignment [11]. The decentralized and immutable nature of blockchain can enhance the transparency, traceability, and accountability of AI systems, thus improving their trustworthiness and ethical governance [12]. However, despite its potential, there remains a lack of comprehensive literature that systematically analyzes the role of blockchain in AI alignment, particularly with regard to its ethical implications.

This gap highlights our pivotal research question: To what extent can blockchain technology, with its unique properties, provide a reliable solution for effectively addressing the challenges of AI alignment? Blockchain is known for its potential to improve security, transparency, and trust in various domains, such as supply chain [13], circular economy [14], or business processes [15]. These attributes could be used to provide a reliable and ethical framework for AI systems, ensuring that they are aligned with human values and subject to effective oversight [11,12]. This systematic review of the literature aims to explore this intersection of blockchain technology and AI alignment by systematically examining the existing research and to evaluate the potential of blockchain to address current limitations in the context of AI alignment. In particular, it will analyze the potential of blockchain to increase the safety of AI systems, reinforce their compliance with ethical standards, and provide a framework for developing innovative AI alignment techniques.

Through a comprehensive review of the literature, this paper identifies the benefits and challenges of integrating blockchain with AI alignment, categorizes existing approaches, and proposes a framework to guide future research in this emerging field. By addressing this key research question, the review contributes to both scientific and practical discussions on the development of AI systems that are not only powerful and effective, but also aligned with the ethical values of human society.

To achieve this, a comprehensive systematic literature review (SLR) is conducted to identify, extract, evaluate, and synthesize existing studies, mapping the current state of research and highlighting gaps in the literature. By developing a structured framework for understanding blockchain’s role in AI alignment, this paper provides a clear and organized overview of existing approaches, categorizing the identified studies and establishing a unified framework that guides researchers and practitioners in effectively aligning AI systems with human values. In addition, it examines key limitations and challenges in the current AI alignment literature, particularly those related to the ethical controllability of AI systems.

Our analysis systematically examines whether blockchain can address these limitations, identifying areas where the unique properties of blockchain, such as its transparency, security, and traceability, could mitigate the risks associated with AI. By contributing to the ongoing discourse on AI development, this paper aims to ensure that AI systems remain powerful and effective while aligned with the ethical values fundamental to human society.

The remainder of this paper is structured as follows: Section 2 reviews the background on AI alignment, blockchain, and ethical frameworks. Section 3 describes our SLR methodology, including our objectives, research questions, search strategy, and selection criteria. Section 4 presents a quantitative analysis of the selected studies, examining publication trends, topic distributions, and sentiment. Section 5 offers a qualitative analysis, identifying key themes, patterns, and intersections across AI, blockchain, and ethics. In Section 6, we develop a three-level taxonomy and propose a comprehensive framework for blockchain-based AI alignment. Section 7 outlines a research agenda by highlighting gaps and posing future research questions. Section 7 discusses the implications of our findings, the limitations of the current literature, and practical considerations. Finally, Section 8 summarizes our contributions and suggests directions for future work.

2. Background

2.1. Related Work at the Intersection of AI, AI Alignment, Blockchain, and Ethics

The reference “A Systematic Literature Review of Blockchain Cybersecurity” [16] provides an analysis of how blockchain technology contributes to improving cybersecurity. Through a systematic review of 42 primary studies, the review identifies and categorizes the most common applications of blockchain in cybersecurity, particularly in the context of the Internet of Things (IoT), data storage, and network security. The report also looks at the potential of blockchain to improve the security of personal data, DNSs, and web applications. In addition, it examines the challenges and limitations of blockchain, such as the energy consumption of certain consensus mechanisms and the complexity of integrating blockchain into existing cybersecurity frameworks. The study emphasizes the need for more robust and scalable blockchain-based solutions to address emerging cybersecurity threats.

The reference “Ethics of Blockchain: A Framework of Technology, Applications, Impacts, and Research Directions” [17] provides a comprehensive exploration of the ethical dimensions of blockchain technology. The authors discuss the key benefits of the technology, such as its decentralization, transparency, and immutability, while also highlighting the ethical challenges that arise from its rapid development and adoption. These include regulatory concerns and trade-offs between privacy and data security. To address these issues, the paper introduces a conceptual framework for analyzing blockchain ethics on three levels. At the micro level, the focus is on the technology itself, examining concerns relating to aspects such as data ownership and privacy. The meso level considers blockchain-based applications, including cryptocurrencies and smart contracts, and their ethical implications in relation to business practices and legal responsibilities, especially regarding automation and liability. At the macro level, the framework considers the broader societal effects, including the potential of blockchain to decentralize governance, reshape economic structures, and transform social institutions. To guide the ethical implementation of blockchain, the authors propose the UTAR (Understanding, Technology, Application, and Regulation) principles, which emphasize a holistic approach to ethical blockchain adoption. These principles are further supported by a call for interdisciplinary research aimed at developing robust ethical standards for emerging technologies. The authors further emphasize the need for interdisciplinary research in developing a robust ethical framework that ensures the responsible adoption of blockchain.

The reference “Converged AI, IoT, and Blockchain Technologies: A Conceptual Ethics Framework” [18] examines the ethical implications of the convergence of three emerging technologies: AI, IoT, and blockchain. The authors argue that, while each technology independently presents significant ethical challenges, their convergence introduces new and complex ethical dilemmas. To address this, the authors develop a framework to analyze these converging technologies and, similar to [17], categorize ethical issues into micro, meso, and macro levels, each maintaining the same focus. The framework proposed by the authors [18] aims to provide a comprehensive approach to understanding and addressing the ethical issues that arise when integrating these technologies. At the meso level, the authors identify new, specific applications enabled by the convergence of these technologies, such as autonomous driving or AI-powered healthcare systems. The authors emphasize the need to refine and expand their framework as these technologies continue to evolve and become more integrated into society.

The reference “Ethics of AI: A Systematic Literature Review of Principles and Challenges” [19] discusses the ethical issues involved in the development and application of AI. The authors highlight the gap between theoretical ethical principles and their practical application. They identify the key ethical principles proposed for AI systems, with transparency, privacy, accountability, and fairness being the most frequently cited in the literature. These principles aim to ensure that AI systems are ethically developed and deployed and are in alignment with societal values. In addition, the authors outline the key challenges for effective implementation of fundamental ethical principles. The most commonly identified challenges include the lack of ethical knowledge among AI developers, the ambiguity of ethical guidelines and principles, and the difficulty of translating these principles into concrete, actionable rules that can be applied for AI development. The authors therefore propose the creation of a maturity model to assess the ethical capabilities of AI systems along with more concrete and context-specific ethical guidelines.

The reference “Safety Assurance of Artificial Intelligence-Based Systems: A Systematic Literature Review on the State of the Art and Guidelines for Future Work” [20] provides an overview of the safety assurance of AI-based systems, motivated by the growing integration of AI into safety-critical applications such as autonomous vehicles and healthcare systems. By reviewing 329 references, the authors identify five main approaches to ensuring the safety of AI-based systems: black-box testing, safety envelopes, fail-safe AI, combining white-box analysis with explainable AI, and implementing a safety assurance process throughout the system lifecycle. The authors discuss the features, benefits, and shortcomings of each approach in detail. The paper also provides guidelines for future research by identifying research topics such as the need for more robust safety assurance methods, better integration of explainable AI into safety-critical systems, and the development of standardized tools for AI safety assessment. The authors suggest developing general, application-independent methods to ensure the safety of AI and expand our understanding of how AI systems can be used safely.

Table 1. Comparison of related surveys. The checkmark indicates that the respective topic is discussed in sufficient depth within the referenced survey. Bold highlight indicates that our review achieves high relevance and systematic rigor while covering AI, AI alignment, blockchain, BC-based AI alignment, and ethics in depth and being the newest SLR.

Survey	Relevance		Topics Discussed in Sufficient Depth
	Year *	SLR **	AI	AI *** Alignment	Blockchain (BC)	BC-Based AI Alignment	Ethics
Ref. [16]	2018	High	✓	-	✓	-	-
Ref. [17]	2018	Low	-	-	✓	-	✓
Ref. [18]	2021	Low	✓	✓	✓	-	✓
Ref. [19]	2021	High	✓	-	-	-	✓
Ref. [20]	2022	High	✓	✓	-	-	-
Our SLR	2024	High	✓	✓	✓	✓	✓

* If available: Year of data retrieval; otherwise, year of submission of the script; otherwise, year of publication. ** Indicates systematic and rigorous methodological approaches. *** AI alignment in a broader sense, and hence also including topics such as AI safety, etc.

summarizes the comparison of our work with other related reviews. It contains a citation column named Survey. The Year and SLR columns establish the relevancy through recency and a High or Low rating, indicating the systematic and rigorous methodological approaches subjectively. The other columns contain a checkmark (✓) if the survey addresses the specified topics in sufficient depth and a dash (-) otherwise. The topics considered are AI, AI alignment, blockchain (BC), BC-based AI alignment, and ethics. Our SLR achieves high relevance and systematic rigor while being the only one that covers all of the following topics in depth: AI, AI alignment, BC, BC-based AI alignment, and ethics.

2.2. Ethical Dimensions of Blockchain and AI Integration

Throughout the past decade, AI safety research has expanded substantially, prompted by the increasing deployment of AI in critical domains such as healthcare and autonomous transportation. Ref. [19] identifies key ethical principles for the development of AI systems, including transparency, privacy, accountability, and fairness. At the same time, blockchain technology is increasingly being utilized to improve cybersecurity across various domains, including AI systems, through the leveraging of its decentralized, transparent, and immutable nature.

The ethical dimensions of blockchain can be analyzed across various levels, including in relation to its underlying technology, specific applications, and broader societal implications. While blockchain facilitates decentralization, transparency, and immutability, it also raises ethical concerns about data privacy and accuracy. In this context, the UTAR principles are introduced as a guide for the ethical implementation of blockchain. The complexity of integrating blockchain into existing cybersecurity frameworks presents a widely recognized challenge. The integration of blockchain and AI adds layers of complexity, requiring a multi-level analysis using conceptual ethics frameworks. Refs. [17,18] therefore suggest categorizing the ethical implications of the convergence of blockchain and AI into micro (technology), meso (applications), and macro (societal impact) levels.

Ref. [20] emphasizes the need for more robust methods and standardized tools to ensure and assess AI safety. In addition, it underscores the need for further research to refine and expand ethical frameworks to ensure that key technologies such as AI are responsibly integrated into society. Currently, the practical implementation of ethical principles for the development of AI systems is challenging, as existing ethical guidelines remain vague and translating broad ethical concepts into actionable steps presents significant complexity. Therefore, more concrete and context-specific ethical guidelines are needed to bridge the gap between theory and practice. Addressing the challenges of AI alignment and ensuring the safe use of AI systems in increasingly complex applications requires interdisciplinary research approaches.

3. Methodology

Many scientific disciplines have established standards for literature reviews, with SLRs being recognized as the most effective approach to fully understand the background necessary to develop rigorous research projects. As an effort to establish a rigorous methodology due to the absence of standardized guidelines for SLRs at the intersection of AI, blockchain, and ethics, this study adopts an approach based on established standards from closely related fields such as software engineering [21] and the medical field, which requires strong ethical principles [22,23]. Our SLR complies with the PRISMA guidelines and the detailed PRISMA checklist is in the Supplementary Materials (Table S1). Additionally, another closely related field, operations research, provides a taxonomy of SLRs [24] and our SLR aligns with the tutorial as it selectively focuses on the AI alignment problem. In the remainder of this section, we show the SLR methodology by explaining the following:

• Our objectives and research questions;
• Our search strategy;
• Our search criteria;
• Our inclusion and exclusion criteria;
• Our search and selection procedure;
• The data extraction and synthesis;
• Important characteristics of the selected primary studies.

3.1. Objectives and Research Questions

Although blockchain has emerged as a transformative technology with significant potential, the current literature lacks a systematic analysis in the context of AI alignment, particularly in terms of ethical implications. To address this gap, this paper investigates the following umbrella research question: Can blockchain technology, with its unique properties, provide a reliable solution to the ethical challenges of AI alignment? Based on this research question, the following research objectives were derived:

• Creation of a reliable and ethical framework for AI systems.
• Alignment of AI systems with human values.
• Effective control of AI systems.
• Identification of the limitations in the AI alignment literature.
• Systematic analysis of whether blockchain can address the limitations in the current AI alignment literature.
• Investigation of the potential of blockchain to increase the security of AI alignment and its compliance with ethical standards.

The first four points outline the general objectives of the AI alignment literature, while the last two provide a more detailed focus for this study.

We planned the review process by refining the research objectives into a set of inclusion criteria research questions (ICRQs):

• ICRQ1: How can blockchain meet the limitations of existing AI alignment approaches fostering ethical technology?
• ICRQ2: What are the potential benefits and limitations of using blockchain technology to govern and enforce AI alignment?
• ICRQ3: To what extent can blockchain-based consensus mechanisms be used to facilitate the development of ethical AI for specific applications?
• ICRQ4: What are the key challenges and research gaps that need to be addressed to effectively integrate blockchain technology with existing frameworks for AI alignment and the development of ethical AI systems?
• ICRQ5: How can blockchain-based consensus mechanisms facilitate human voting on ethical principles for AI development, promoting better alignment with human values?
• ICRQ6: How can blockchain-based consensus mechanisms/blockchain technology facilitate human involvement in ethical decisions on AI alignment?
• ICRQ7: How can blockchain-based consensus mechanisms/blockchain technology improve human oversight in AI decision-making processes, promoting better alignment with ethical principles?

3.2. Search Strategy

We utilized the Scopus database to analyze the available manuscripts. Scopus is a comprehensive summary and citation database of peer-reviewed literature, including scientific journals, books, and conference proceedings. It contains content from thousands of academic publishers and is recognized for its comprehensive coverage of disciplines such as science, technology, medicine, social sciences, and arts and humanities. Scopus provides tools for tracking, analyzing, and visualizing research results, making it a valuable resource for conducting literature reviews and citation analysis. The database, managed by Elsevier, is widely utilized by researchers, institutions, and academic libraries worldwide.

The parameters for our search strategy selected peer-reviewed articles written in English across the following categories: journal articles, book chapters, conference proceedings, and books/eBooks. The detailed specification of the search domains included blockchain, ethics, and AI safety. To capture all relevant domains, the search was conducted using all fields to ensure that the largest possible number of relevant publications could be identified. In addition, we did not limit the publication date as “AI alignment” and “blockchain” are new technologies anyway. Limiting the study to more recent years could have led to important earlier studies being overlooked.

3.3. Search Criteria

The search criteria required specific keywords relevant to our study. Therefore, our search query consisted of several groups of terms in conjunctive normal form to capture a comprehensive range of relevant publications. The first group, (blockchain OR dlt), with keywords ‘Blockchain’, ‘DLT’, and ‘Distributed ledger’, targeted general topics related to blockchain technology or distributed ledger technology (DLT). The second group, (AI OR artificial intelligence), had keywords ’Artificial Intelligence’, ’AI’, ’Machine Learning’, and ’ML’. The third term, ethic(s), with keywords ‘Ethic’, ‘Ethics’, ’AI Safety’, ’AI Alignment’, and ’Human Values’ narrowed the focus to ethical considerations and specified precise terms of interest by targeting the literature that discusses the respective topics. By combining these groups, the search query was designed to ensure a thorough examination of the overlap between these topics.

3.4. Inclusion and Exclusion Criteria

Information about the selected studies from the electronic databases was assessed using the inclusion and exclusion criteria explained in this subsection.

3.4.1. Inclusion Criteria

In a first step, the following inclusion criteria (IC) were applied:

• IC1: Study language is English;
• IC2: Study includes at least one keyword from each keyword domain: AI, blockchain, and ethics;
• IC3: Study is either a peer-reviewed journal article, a book chapter, conference proceedings, or a book/eBook.

After the inclusion criteria were met, the following metrics were defined to allow for an initial screening of the studies:

• Whether quality criteria (Methodology, Clarity, Completeness, Transparency) were met;
• Whether inclusion criteria research questions (ICRQ1, ICRQ2, …, ICRQ7) were answered.

3.4.2. Exclusion Criteria

In the next step, the following exclusion criteria were applied when screening the studies:

• EC1: Studies that do not meet the quality criteria;
• EC2: Studies that do not answer any of the ICRQs;
• EC3: Studies that do not meet all inclusion criteria.

Further exclusion criteria were applied to the remaining studies in the course of the in-depth analysis:

• EC4: Studies where the full text could not be retrieved;
• EC5: Studies where the full text could not be retrieved in English;
• EC6: Studies that are too short in length;
• EC7: Studies that do not discuss the intersections between AI, blockchain, and ethics to a sufficient extent.

3.5. Search and Selection Procedure

The search and selection procedure is shown in Figure 1. Within the initial search based on the search query described above, 7110 papers were initially selected on 28 May 2024. After applying the first three inclusion criteria (IC1-IC3), the initially retrieved 7110 papers were reduced to 219. Those 219 studies were subjected to an initial relevance assessment using the exclusion criteria (EC1-EC3) by 22 Penn State Great Valley students. That process excluded 155 studies. The remaining 64 studies were again filtered via applying the additional exclusion criteria (EC4-EC7) by the authors and Dwight Smith to ensure that only highly relevant studies ($n=46$) were analyzed in the remaining sections of this review. The final selection was made by the first author and confirmed by the other authors.

3.6. Characteristics of Selected Primary Studies

Based on the filters and inclusion exclusion criteria applied, we started with 219 papers from the Scopus database. After filtering based on the scope of the literature review and other factors, we were left with 46 papers. We will briefly discuss the papers that were included in this study to explain how we could classify and derive insights from the literature and the topics they discuss. As an initial step, we classified the studies according to the type of publication. Our findings are presented in Figure 2.

The figure contains the distribution of studies per year (dotted line), with their number (maximum 8) indicated on the right side. For each type, the number of studies is written in the circle of the corresponding color. As shown in Figure 2, the distribution of publication types from 2017 to 2024 reveals several key trends in the selected primary studies. Journal articles dominated the landscape, with consistently high numbers across all years. Their frequency increased from 6 in 2019 to a peak of 8 in 2022, followed by a slight decline to 7 in 2024, reflecting the sustained importance of journals as the primary venue for scholarly dissemination.

Conference papers demonstrated moderate fluctuation. Starting with 1 in 2018, they peaked at 5 in 2023, suggesting their continued relevance in fast-evolving fields, although with fewer contributions than journal articles. Book publications remained minimal, with only one exception in 2020. Book chapters, similarly sparse, appeared solely in 2024 with just one publication, highlighting their more peripheral role in this research domain.

The dotted line tracking the total number of publications each year reveals a general upward trend in research activity peaking in 2023 before slightly dipping in 2024. This trajectory reflects a growing academic interest and output in the area under study, with journal articles consistently serving as the dominant dissemination medium.

Meanwhile, conference reviews, reviews, and short surveys remained relatively low in frequency, with modest increases over time. Notably, erratum and retracted papers emerged only in 2023, albeit in minimal numbers, which may indicate increased scrutiny or corrections in the field. The editorial category appeared for the first time in 2024, suggesting an evolving role of editorials in academic discourse. These trends highlight the shifting landscape of knowledge dissemination, with articles and conference papers maintaining dominance and books and reviews providing supplementary insights. The recent emergence of retractions and errata may also reflect evolving academic integrity standards and quality control measures in research publications.

The methodological rigor of the reviewed papers improved significantly over the years. In 2018, only 3 papers met the strong methodological criteria, but this number increased to 27 in 2023, highlighting a growing focus on robust research designs. However, the slight decline in 2024 (16 papers) suggests variability in methodological adherence.

Clarity, which measures how well studies communicate their findings, saw a notable rise, peaking with 31 papers in 2022. This suggests that 2022 was a pivotal year for clearly articulated research. However, the decline in 2023 (25 papers) and 2024 (19 papers) may indicate the increasing complexity in the research topics, requiring more precise communication strategies.

The Completeness of the studies, assessing whether all necessary details such as the methodology, results, and limitations are reported, followed a similar pattern. It peaked in 2022 (24 papers) but then declined in 2023 (17 papers) and further to 9 in 2024. This suggests potential concerns regarding the thoroughness of reporting in recent studies.

Transparency, a key factor for reproducibility, remained relatively stable, with moderate increases from 2018 (5 papers) to 2022 (17 papers). Unlike Completeness, Transparency scores remained consistent in 2023 (16 papers) and 2024 (13 papers), indicating an ongoing commitment to openness and reproducibility.

In the next two sections, we systematically cover quantitative and qualitative analyses of the selected studies, examining publication trends, topic distributions, and sentiment and identifying key themes, patterns, and intersections across AI, blockchain, and ethics.

4. Quantitative Analysis

The included studies were first quantitatively analyzed to form initial assumptions about their characteristics. Quantitative analysis helped identify commonalities and patterns in the selected articles. In addition, the identified topics could be categorized, which supported the subsequent qualitative investigation and contributed to the development of the taxonomy presented in the later sections of this work.

Table 2. Number of included papers per year.

Publication Year	Number of Papers	References
2024	8	[25,26,27,28,29,30,31,32]
2023	10	[11,33,34,35,36,37,38,39,40,41]
2022	8	[42,43,44,45,46,47,48,49]
2021	4	[50,51,52,53]
2020	7	[17,54,55,56,57,58,59]
2019	8	[60,61,62,63,64,65,66,67]
2018	1	[68]

showcases the number of papers published per year. It was observed that the number of papers published in 2019 was sharply increased compared to the previous year. Since then, a similar number of papers have been published almost every year, with the exception of 2021, when only four papers were published.

Figure 3 shows the total number of included papers per publication year and indicates a positive trend highlighted by a linear trend line.

4.1. Topic Analysis

An initial analysis of the included articles, as shown in Table 2, reveals the following key observations: The key role of ethical considerations in the development and deployment of blockchain, AI, and their overlaps is underlined by frequently occurring terms such as “ethics”, “ethical implications”, “morality”, “privacy”, “security”, “trust”, and “governance”. Almost a fifth of the selected studies highlight the use of blockchain and AI in healthcare. The focus of these studies is on areas such as improving patient care, managing medical records, and facilitating drug development. Blockchain and AI are increasingly used to improve transparency in supply chains, mitigate the spread of misinformation, and optimize government services, thus contributing to the common good. The most frequently mentioned features associated with blockchain and AI are trust and transparency. In addition, blockchain technology is applied to improve trust and transparency in various domains, including but not limited to supply chains, healthcare, and financial systems. The included studies also indicate that concepts such as “explainable AI” and “decentralized AI” are becoming trending areas of research.

When searching for co-occurrence keywords in the included papers, interesting findings can be retrieved. Therefore, synonyms are considered for “AI” (e.g., “machine learning”, “deep learning”, “AI systems”, “artificial intelligence”), “Blockchain” (e.g., “cryptocurrency”, “distributed ledger technology”, “smart contracts”), and “Ethics” (e.g., “moral”, “ethical implications”, “social impact”, “privacy”, “security”, “governance”). “AI” frequently appears alongside “ethics”, “privacy”, and “bias”, underscoring that the ethical implications of AI, particularly in relation to privacy, algorithmic bias, and its potential to increase social inequalities, are central concerns. As mentioned above, “healthcare” frequently appears alongside “blockchain” and “AI”, highlighting their significant potential in this field. In addition, the terms “governance” and “regulation” appear in strong conjunction with the terms “blockchain” and “AI”. This suggests a growing awareness of the need for robust governance frameworks and regulations to guide the ethical and responsible development and adoption of these technologies.

4.2. Polarity vs. Subjectivity by Topic

An initial generic analysis reveals the relevance of ethics, governance, and social aspects in the context of blockchain and AI. Subsequently, more advanced analysis methods are applied to the included papers. For this purpose, sentiment analysis (TextBlob) [69] and topic modeling (LDA) [70] are performed to understand the language and primary thematic clusters in the corpus. Figure 4 contains all selected studies grouped into topic thematic clusters by the tools we applied. The figure illustrates how each paper’s polarity (x-axis) and subjectivity (y-axis) vary. Topic thematic clusters are color-coded by the five main topics in the developed LDA model:

• Data Privacy, Security, and Fairness;
• Blockchain Supply Chain and Humanitarianism;
• Blockchain and AI Ethics Research;
• Governance, Healthcare, and Corporate AI;
• Blockchain and Social-Ethical Aspects.

A higher polarity indicates more positive or optimistic language, while a lower (negative) polarity reflects a more critical or cautionary tone. For instance, studies in Blockchain and AI Ethics Research (Topic 3) tend to exhibit moderate-to-high positivity. Those emphasizing the challenges or limitations of blockchain (e.g., adoption barriers, ethical pitfalls) tend to display more neutral or negative language, as seen on the left side of the plot. Similarly, a higher subjectivity score often corresponds to more opinion-based or normative discussions around governance, fairness, and social responsibility.

4.3. Distribution of Studies Across Topics

Figure 5 illustrates the distribution of the selected studies across the five emergent themes discovered by the tools in Section 4.2 related to AI alignment in blockchain contexts. The most prominent category is Blockchain and Social-Ethical Aspects (Topic 5), with over 20 papers, highlighting a strong emphasis on societal implications, ethical values, and alignment challenges in decentralized technologies. The Governance, Healthcare, and Corporate AI (Topic 4) and Data Privacy, Security, and Fairness (Topic 1) topics also have considerable representation, underscoring the ongoing interest in institutional alignment mechanisms and fairness-preserving AI integration. In contrast, certain application-specific topics, such as Blockchain Supply Chain and Humanitarianism (Topic 2), are represented by fewer studies. This trend reflects a field still prioritizing foundational alignment discourse over domain-specific deployment strategies. This indicates that, while general ethical and social concerns dominate the literature, specialized areas (supply chains, corporate governance, healthcare) represent smaller but still important sub-fields for ethical AI–blockchain integration.

4.4. Average Polarity by Topic

Figure 6 shows each topic’s mean polarity. Notably, Blockchain and AI Ethics Research (Topic 3) and Data Privacy, Security, and Fairness (Topic 1) score relatively highly, suggesting that authors who emphasize constructive solutions, transparent governance, and ethical compliance have a generally optimistic view. In contrast, topics involving governance or supply chains exhibit more cautious or technically focused language, yielding a slightly lower average positivity.

This analysis reveals several insights. Studies with a focus on social, ethical, or moral frameworks for blockchain and AI tend to display relatively positive or forward-looking sentiments, implying optimism about harnessing the technology for human-centric outcomes. Approximately $30\%$ of the studies that emphasize risks, threats, or barriers in blockchain adoption appear more neutral or negative, reflecting caution or critique. The quantitative distribution of topics suggests that ethics, social considerations, and data privacy are central themes in our corpus, while application-specific topics (supply chains, governance, etc.) feature fewer, but still meaningful, studies. Finally, the average polarity analysis underscores that technical areas (like Governance, Healthcare, and Corporate AI) incorporate pragmatic or regulatory hurdles, resulting in a slightly lower overall positivity than more conceptual or solution-oriented topics.

5. Qualitative Analysis

Following the quantitative analysis of the previous section, Section 4, which identified initial overlaps and patterns in the included literature, the studies are qualitatively examined. This qualitative analysis is essential for a deeper understanding of the concepts and relationships in the included studies. It allows for the identification of recurring themes, subtle patterns, and underlying assumptions that are typically overlooked in quantitative methods. A deeper understanding of the literature enables the creation of a more refined and meaningful topic classification and facilitates the creation of a more precise taxonomy. Ultimately, the qualitative analysis increases the validity and applicability of the resulting taxonomy and ensures that it more accurately reflects the complexity of blockchain-based AI alignment.

5.1. Ethical AI Frameworks

The autonomous nature of generative AI requires interdisciplinary ethical frameworks and robust guidelines to ensure reliable and unbiased AI systems that prioritize human well-being and dignity. Ethical AI development therefore requires issues such as privacy, bias, and accountability to be addressed, especially in sensitive areas such as healthcare. The integration of blockchain into AI can increase transparency and trust in AI systems, while frameworks such as the Asilomar AI Principles [62] and weighted ethical checklists provide guidance for responsible AI system development.

Although traditional software systems are developed to perform specific tasks and therefore can be fully trusted, generative AI is developed to perform a wider range of tasks in a more autonomous way, leading to trust issues [38]. Hence, to address complex ethical issues and promote trustworthy AI, an interdisciplinary approach is required to establish mechanisms to implement ethics within AI engineering and trustworthy AI systems [50]. Sound and resilient AI policies must be implemented to increase AI safety and ensure that AI decision-making processes are unbiased, fair, and equitable [37].

An AI system can only be considered trustworthy if it contributes to human prosperity and dignity and benefits humanity [50]. Hence, AI development should be guided by human rights and the needs of a society, and should incorporate democratic values [37]. For example, trustworthy AI applications in healthcare can improve the accuracy of medical diagnoses, but must do so ethically by addressing central concerns such as privacy, bias, and liability, which require regulations to be implemented and ethical rules to be followed [35].

At an abstract level, an ethical AI framework must consider threats arising from biased AI models, black-box AI decision-making processes, and accountability for AI decisions, together with privacy, security, and dignity concerns for users [50]. Technological innovation must align with ethical obligations, requiring the integration of principles such as privacy-by-design principles, which are embedded in the system [30]. A holistic framework to promote a trustworthy, transparent, and accountable AI system is proposed by combining AI with other technologies and disciplines such as blockchain, big data, and IoT [37]. Another framework is proposed that integrates blockchain technology into multi-agent systems, which enables greater trust, transparency, and accountability in distributed intelligent systems, eliminating reliance on trusted third parties and fostering more robust and ethical AI ecosystems in critical domains such as healthcare and finance [66].

A further relevant ethical framework that should be considered in AI system development is the Asilomar AI Principles, discussed in the context of software development lifecycles [62]. Among other things, the framework describes principles for “safety” and “value alignment”, which require the verifiability of the AI system in the development and deployment phases, and alignment to human goals throughout its entire operation [62]. In addition, fundamental elements of digital ethics are data protection, data ownership, data accuracy, and data access, based on which the ethical implications of emerging technologies such as blockchain and AI can be assessed [38]. A weighted checklist of key ethical principles, including accountability, fairness, privacy, and the right to be forgotten, can be used to assess a technology’s inherent ethical compliance [33]. When combined with external factors, such as the complexity of the infrastructure in which the technology is implemented or the effectiveness of cybersecurity measures, a more objective analysis of ethical compliance becomes possible [33].

5.2. Blockchain as a Basis for Ethical AI

Blockchain technology provides transparency, security, and efficiency across diverse domains, enabling decentralized collaboration, ethical data sharing, and the democratization of AI development. The synergy of blockchain with AI improves the explainability and accountability of AI systems. Smart contracts can be used to codify ethical rules, with the aim of creating “ethical machines”. Human-in-the-loop systems and Proof of Personhood (PoP) mechanisms are crucial for AI governance, particularly in Artificial General Intelligence (AGI) scenarios, to ensure humans maintain control over these AI systems. Securing sensitive data in IoT and cyber–physical social machines through blockchain is essential for ethical AI applications, preventing data tampering and misuse. Blockchain-based reputation systems and token incentives can further promote ethical behavior and data integrity within blockchain-based AI systems. Integrating blockchain with corporate governance can mitigate information asymmetries and enhance auditing processes, promoting transparency and ethical operations. Blockchain’s ability to enable secure data transactions and ensure the authenticity of AI input data underlines its importance as a basic building block of trustworthy and ethical AI systems.

Blockchain offers transparency, security, and efficiency in various areas such as financial services, identity systems, and voting systems [45]. Blockchain-based smart contracts can be used to govern interactions through business and security rules, eliminating centralized solutions and data silos and instead enabling decentralized collaboration and data sharing between all participants that can be trusted [37]. For example, in blockchain-based multi-agent systems, smart contracts govern the interactions of the agents and are used to resolve disputes between the agents [66]. Furthermore, smart contracts can contribute to a more ethical design of property management in the digital space by improving the monetization of IP rights for individuals [38].

The synergy of blockchain technology with AI can bring advantages such as explainability, privacy, scalability, and personalization [28]. Blockchain can be considered a foundational technology that enhances the ethical considerations of AI systems by offering transparency, immutability, and accountability [68]. A blockchain-based AI control system can encode moral rules into smart contracts to enforce ethical behavior, ensure compliance with security standards, and facilitate automated configuration management [63].

Kant’s categorical imperative is a suitable ethical framework for blockchain-based smart contracts, as it sets strict moral rules that can be codified, potentially making blockchain not only a “trust machine” but also an “ethical machine” [47]. For example, embedding Kant’s ethical framework in smart contracts promotes ethics in communication between participants on blockchain-based platforms [55]. However, applying Kant’s categorical imperatives in translating complex human moral judgments into computer code may be insufficient when dealing with the complexity of ethical dilemmas and nuances in real-world AI applications [47].

In real-world situations, ethical decision-making depends on technology, human tendency, and the situation, requiring a dynamic ethical framework where human control and virtue ethics are integrated into the system [47]. An AI system becomes human-centered and trustworthy if it respects the dignity and autonomy of humans [50]. In this respect, human–computer interaction is identified as a relevant research stream, linking blockchain technology with human values and lived experience [68]. This, in turn, helps us to design and implement systems which prioritize human concerns, ethics, and fairness [68].

A system that allows human agents to vote on AI rules while being protected from interference by AI agents is particularly important in AGI scenarios [11]. Thus, a PoP consensus mechanism can be used to differentiate between humans and AI agents, for example, by facilitating a combination of government identity mechanisms and biometric features [11]. Worldcoin, for instance, provides proofs of a person’s uniqueness through an eye scan while preserving privacy, as no user data need to be stored permanently due to the implementation of Zero-Knowledge Proofs (ZKPs) [38].

Blockchain contributes to the democratization of AI research and development, which is currently limited to a few tech giants, allowing the inclusion of human values and promoting positive technological change, also in future AGI scenarios [61]. Blockchain allows the integration of ethical rules to ensure the consistency and immutability of AI values, for example, allowing petitions for the denial of resources in the case where an AI agent does not comply with these values [63]. Therefore, sophisticated identification methods, implemented in the previously mentioned PoP mechanism, can be used to verify humans and AI agents [11,63]. This ultimately enables a secure AI ecosystem that incorporates checks and balances [63].

Algorithmic accountability is increasingly important, especially for generative AI systems and their underlying ML models [68]. Blockchain provides an immutable and decentralized record of AI outputs [63]. Hence, blockchain has the potential to hold algorithms accountable due to its transparency and immutability features, and therefore can be used to ensure that AI systems and ML models are held accountable for their decisions and actions [68].

The problem of the use of private data in AI, and, more generally, the tension between human ethics, regulation, and the potential benefits of AI, is highlighted [59]. Possible solutions are distributed ledger or blockchain systems and open data spaces, such as private data used as input for AI systems which are written on a blockchain to strengthen trust and thus human ethics [59]. For example, blockchain can be used to address security challenges in digital environments such as the Metaverse, where blockchain enhances data security by protecting sensitive user information from unethical practices and misuse [34]. In addition, blockchain enables the development of a transparent and decentralized data system that ensures the ethical and responsible processing of private data [60]. Furthermore, the use of blockchain in cyber–physical social machines fosters trustworthy AI systems by ensuring that AI applications rely on reliable IoT input data that have not been tampered with [67].

Integrating blockchain technology in cyber–physical social machines offers an innovative approach to building autonomous distributed systems, mitigating risks arising from the large number of sensitive data collected by IoT devices within such systems, and ensuring that user data are protected from manipulation, e.g., tampering, and theft [67]. On a blockchain, transaction data are stored in a clear chronological order, enhancing data transparency [31]. Blockchain ensures data authenticity and integrity through collective validation, improves accuracy, strengthens trust in fields such as supply chains and healthcare, and promotes ethics in the digital space [38]. The blockchain architecture allows users to gain control over their data by encrypting transaction data with their own private key or signing them with their unique digital signature [31].

In addition to benefiting IoT and cyber–physical social machines, blockchain also improves organizational and auditing processes through new governance mechanisms. Integrating blockchain with corporate governance theories has the potential to mitigate information asymmetries and agency costs through increased transparency [57] while reducing conflicts between agents and organizations due to its self-controlling properties [25]. In human resource management, blockchain can improve internal processes, for example, through the use of network-based conflict resolution [48]. Audits can benefit from encrypted signatures and the decentralized verification offered by blockchains [39]. In terms of market mechanisms, smart contracts can facilitate real-time auditing and improve market efficiency [57]. Open-source blockchain technology improves morality, ethics, and sustainability in organizations through blockchain governance, further reducing information asymmetries between participants [25]. AI could improve the blockchain-enabled decision-making process in organizations by deciding which external data enter the system and using its analytical capabilities to detect biased, inconsistent, or unreliable data [48]. If the implementation of blockchain in organizations is performed carefully, organizational processes can become more efficient and transparent, and a more ethical working environment can be created [48].

Since trust is an important factor in multi-agent distributed intelligent systems, a blockchain-based reputation system is proposed that aims to strengthen trust between agents by securely and transparently recording their behavior; however, potential privacy issues need to be addressed [66]. In such reputation systems, the immutable and transparent properties of the blockchain can be combined with rewarding truthful behavior and punishing misleading actions [44]. Participants have a dynamic reputation value, starting at zero, which increases for correct decisions and decreases for incorrect decisions [44]. Reputation system rewards could include token incentives. Data quality measures such as accuracy and contextualization of data underscore the importance of ensuring data integrity in AI applications, which could be improved by transparent incentive mechanisms on the blockchain [49]. In particular, blockchain-based tokens can be used as crypto-economic incentives, which can promote ethical data sharing and privacy in AI systems [49]. Tokenized incentives that incorporate game theory can, in turn, be used to align AI behavior with human interests [63].

5.3. Blockchain-Based AI Use Cases

This section describes the use cases of blockchain-based systems and their positive impact on the AI applications that operate on them. The immutable ledger and decentralized nature of blockchain improve data integrity and security in various domains and are essential for AI applications that rely on trusted input data. In healthcare, blockchain ensures the secure exchange of patient data and improves AI-assisted diagnostics while maintaining patient privacy. AI systems in education benefit from the protection of sensitive student data through blockchain. To combat fake news, blockchain helps verify the authenticity of content, prevents manipulation, and promotes accountability. In financial services, blockchain’s immutable record of transactions provides the transparent and verifiable data required for AI-powered fraud detection. In supply chains, the traceability of blockchain enables AI-assisted detection of counterfeit products while maintaining sustainable and ethical practices. In the public sector, secure, tamper-proof data storage via blockchain is essential, especially for sensitive information such as the biometric data of refugees, for AI applications that need to ensure data integrity and protect individual rights.

In healthcare, cybersecurity and ethics are linked, as patients can be harmed by and thus lose trust in poorly protected healthcare systems [30]. Blockchain can be used for secure, decentralized, immutable electronic medical records to prevent loss of medical data and fraud and allow secure data sharing of clinical research data [32]. Smart contracts can automate the processing of health insurance claims [32]. Blockchain technology can also be used to manage consent forms in the context of clinical trials [56]. Implementing blockchain–digital twin technology in the healthcare sector can benefit health data management and security [40]. Digital twins also allow patients to be more directly involved in monetizing their data, thus promoting self-sovereignty [40]. Blockchain helps create a complete audit trail for clinical data and facilitates compliance with data protection regulations and ethical standards [56].

The use of blockchain in the medical field facilitates the exchange of patient data between different healthcare providers and increases the quality and authenticity of medical data, which, in turn, allows AI applications in the healthcare sector to produce more accurate results [35]. Furthermore, blockchain improves remote patient monitoring by enabling secure real-time IoT-based health observation [32], which makes maintaining patient data confidentiality particularly important due to the extensive collection and transmission of sensitive health data [30]. In such scenarios, blockchain can protect patient data and help ensure that ethical standards are met when the aforementioned data are used, for example, for AI-supported real-time monitoring and decision support [53]. Blockchain, in combination with other cryptography-based technologies such as ZKPs, can contribute to the ethical processing of healthcare data by enabling AI applications to learn from sensitive healthcare data without violating patient privacy rights [52].

Blockchain can support the ethical development of AI applications in healthcare by maintaining the integrity of medical data and ensuring that consent processes are fully traceable and verifiable [56] while the risk of unauthorized data use is reduced [52]. When applying a combined approach to assess the ethical compliance of technology, blockchain-based healthcare systems that incorporate design choices such as off-chain storage of sensitive data and encryption achieve higher evaluation scores [33]. As an example, an organ transplantation use case is implemented, leveraging features such as decentralization, security, traceability, auditability, and privacy enabled by the underlying blockchain technology [43]. In addition, the privacy, security, and confidentiality of the solution are evaluated and compared with existing systems to demonstrate how the blockchain can improve trust and accountability [43].

In AI-powered educational systems, blockchain technology can help mitigate ethical challenges. The decentralized nature of blockchain combined with cryptographic techniques can protect sensitive educational data from unauthorized access and breaches, ensuring data privacy and security [41]. Blockchain can create a transparent and replicable record of data usage, for example, in educational AI applications, ensuring that data are used ethically and responsibly and fostering accountability [41]. Blockchain can enable secure and efficient data sharing between different educational institutions, promoting collaboration and knowledge sharing [41]. Furthermore, the immutability of the blockchain and its resistance to tampering can create trust in the authenticity and integrity of educational data, promoting reliability [41].

As advanced AI models contribute to the spread of fake news, blockchain-based systems can enhance social media news verification by permanently recording data, preventing manipulation and promoting accountability [44]. In such systems, the blockchain ensures the authenticity of news content by creating tamper-proof records and verifying the origin, integrity, and authenticity of news [46]. Therefore, the blockchain acts as an immutable and time-stamped database that prevents users from altering their activities, such as changing votes or news content retrospectively [44]. A consensus algorithm inspired by the “Byzantine Generals Problem”, which requires a majority agreement among the participants, is proposed to vote on the truthfulness of news [44]. In addition, a truth validation mechanism for social media platforms based on Kant’s moral formalism and consensus mechanism is implemented in the logical structure of blockchain-based smart contracts to allow for decentralized detection of fake news [55].

In addition to helping detect fake news, smart contracts can also improve financial services. Blockchain-based smart contracts enable automated condition-based financial transactions without intermediaries, reduce processing time and transaction costs, and streamline financial processes [58]. Financial services could benefit from higher reliability if double-entry bookkeeping is extended with blockchain [39]. In addition, blockchain enables a decentralized finance (DeFi) economy that aims to increase financial inclusion and strengthens the ethical implications of this technology for financial services [38].

In the supply chain, blockchain increases the authenticity of data, reduces information asymmetries, reduces the power of central players, and helps smaller players access market information [42]. The integration of blockchain in supply chains strengthens the trust of consumers in products and promotes ethical practices among supply chain participants through decentralized verification [36]. In drug supply chains, for example, the use of blockchain can make the manufacturing process of medicine more transparent and traceable, which, in turn, increases patient safety [35]. The decentralization aspect of blockchain, increased transparency, and the reduction of information asymmetries along the supply chains reduce unethical practices and promote sustainability [42].

Finally, blockchain can improve various public sector applications, such as by reducing fraud and increasing transparency in carbon credits, or improving security in voting and identity systems [45]. Blockchain is also discussed in the context of refugee registration, where it could help ensure that biometric data are securely and immutably stored and accessible [64]. A framework for developing blockchain-based humanitarian projects is proposed that takes into account technical requirements, such as decentralization and scalability, context-related requirements, such as ethics and privacy, and organizational requirements, such as interoperability with legacy systems [51]. The ethical requirements, as part of the proposed framework, are critical to protecting humanitarian principles, privacy, and security, preventing harm, and promoting diversity and respect [51].

To summarize the diverse applications and their ethical contributions,

Table 3. Identified blockchain-based AI use cases and their contributions to ethical AI.

Identified Use Cases (Alphabetically)	AI Application Context	Foundational Ethical Contributions				Operational Ethical Contributions			Individual Ethical Contributions
		Decentralization	Immutability	Transparency	Data Integrity/ Authenticity	Security	Traceability/ Auditability	Accountability	Consent Management	Privacy (ZKPs)
Education	Student Data Management and Usage Tracking	✓	✓	✓	✓	✓	✓	✓	(✓)	(✓)
Fake News	News Content Verification and Detection	✓	✓	✓	✓	-	✓	✓	-	-
Financial Services	AI-Powered Fraud Detection, Automated Transactions	✓	(✓)	✓	✓	✓	✓	(✓)	-	-
Healthcare	AI-Assisted Diagnostics and Monitoring, Secure Data Sharing for AI, Ethical Data Processing	✓	✓	✓	✓	✓	✓	✓	✓	✓
Public Sector	Refugee Identity Management, Voting Systems, Carbon Credits Management	✓	✓	✓	✓	✓	(✓)	(✓)	-	-
Supply Chain	AI-Assisted Counterfeit Detection, Traceability and Optimization	✓	(✓)	✓	✓	(✓)	✓	(✓)	-	-

(✓) This property is not explicitly named but is strongly implied or inferred from the text’s description of blockchain’s role in the respective use case.

provides a comprehensive overview of the identified blockchain-based AI use cases, their AI application contexts, and the specific ways blockchain properties and benefits contribute to ethical outcomes. The Identified Use Cases column alphabetically lists the specific areas in which blockchain-based AI systems are used. The AI Application Context column describes the specific areas or systems in which AI is integrated or in which AI input data are managed. The Foundational Ethical Contributions column describes the inherent characteristics of blockchain, such as decentralization and immutability, which serve as core building blocks for ethical AI systems. The Operational Ethical Contributions column highlights the benefits that blockchain brings to the operation of AI applications, such as increased security and traceability, which are relevant to ethical behavior. The Individual Ethical Contributions column focuses on ethical considerations that directly affect individuals, such as consent management and data protection, which blockchain can help to ensure.

The analysis summarized in Table 3 shows that blockchain technology makes ethical contributions in all identified AI application contexts, ranging from foundational contributions, such as decentralization and immutability, to operational contributions, such as increased security and traceability, to direct individual ethical contributions such as consent management and privacy. While healthcare consistently benefits from all key characteristics of blockchain, thereby supporting comprehensive ethical AI implementation, other areas such as education also have significant, though sometimes only indirect, contributions. In contrast, in areas such as fake news and financial services, which benefit greatly from the foundational ethical contributions and operational ethics contributions of blockchain, there is currently little evidence that blockchain is being used for individual ethical contributions such as consent or privacy.

5.4. Challenges in Integrating Blockchain and AI

The existing research often lacks thorough ethical analysis, which leads to overlooked potential societal impacts of blockchain applications. For the ethical use of blockchain-based AI systems, accountability needs to be addressed through dynamic consent models and clear legal frameworks, which currently suffer from the lack of global standards and frameworks and the resulting interoperability issues. In addition, high investment costs, lack of know-how, and organizational resistance hinder the introduction of blockchain. Technical challenges such as scalability, transaction latency, and energy-intensive consensus mechanisms, as well as the potential vulnerabilities of smart contracts, further complicate blockchain adoption. The immutability of blockchain and its smart contracts raises control and liability issues, especially when AI systems are based on it. In addition, the inherent transparency of blockchain poses privacy risks, requiring advanced cryptographic techniques and careful system design to balance efficiency, privacy, and fairness. As a result, the need to address the ethical and technical adoption challenges of blockchain-based AI systems is emphasized, while practical implementations, especially within the Ethereum ecosystem and permissioned blockchains such as Hyperledger Fabric, demonstrate the potential of the technology.

Although blockchain offers various technical advantages such as those described above, it can only be used ethically in combination with other means, including non-technical measures [29]. These measures include dynamic consent models and data ownership frameworks, as well as clear regulations for smart contracts and ownership in blockchain-based systems to ensure accountability [29]. However, legal frameworks are inconsistent at the global level, putting into question the feasibility of cross-border blockchain applications [58]. In healthcare, for example, this leads to interoperability issues, as the lack of standardized frameworks leads to integration problems between different platforms [32]. Interoperability with existing systems is also a challenge for the integration of blockchain-based systems in biomedicine [29], and, for example, in enterprise resource planning [39].

Barriers to integrating blockchain in public systems have been analyzed from different perspectives, for example, from social and ethical points of view [26]. Some identified adoption barriers are the lack of skilled workers and privacy concerns [26], which underscores the need for comprehensive, ethical considerations in the design and deployment of blockchain-based systems [45]. In supply chain use cases, high investment costs, lack of digital expertise, and organizational cultures resistant to change among supply chain participants make the introduction of blockchain technology difficult [36]. In addition, blockchain development can be in the hands of a small team of core developers and centrally managed foundations [68]. To mitigate high initial investments and encourage innovation, supply chain participants could deepen their collaboration by sharing the necessary costs and know-how to adopt blockchain technology [36].

Some challenges in the adoption of blockchain technology are of a purely technical nature. For example, many blockchains have limited scalability, which can lead to network congestion in financial applications [58] or healthcare platforms [32]. Slow transaction processing time could also cause latency issues for real-time operations [32]. In addition, some public blockchains are still based on energy-intensive consensus mechanisms, which can result in economic and ecological issues [58]. Regarding the correctness of the implemented software, blockchain-based multi-agent systems are based on smart contracts, which are prone to malfunction due to complex patterns of autonomous agent interactions, complicated debugging, and system maintenance, making system reliability and verification a prerequisite [66].

Despite the claims of ethical design considerations in blockchain-based systems, the current research often lacks in-depth ethical analysis and does not fully consider the ethical consequences of proposed solutions [54]. Although blockchain offers decentralization and transparency, a framework for studying the ethical aspects of blockchain technology is proposed to analyze ethical challenges on three different levels. The blockchain technology itself comprises the micro level, blockchain-based applications the meso level, and the societal impact of blockchain the macro level [17]. Depending on the use case and application of blockchain technology, blockchain can serve ethical purposes or lead to ethical challenges [27]. Furthermore, the adoption of blockchain can result in potential biases encoded in blockchain protocol [68]. For example, blockchains can unintentionally reinforce discrimination through biased AI data inputs that could codify systemic bias in public services [45].

The ability to make certain aspects of the protocol immutable raises concerns about control of the system and raises ethical questions as to whether these data compromise privacy or security [65]. For example, while blockchain has advantages in storing biometric data in refugee cases, it could also amplify data errors, as these are difficult to correct once entered into the ledger and could exacerbate existing biases when combined with AI applications [64]. Therefore, not all aspects of a blockchain protocol should necessarily be immutable, and deciding which parts are open to change requires careful consideration of the ethical implications for all stakeholders [65]. Although blockchain-based smart contracts are immutable and can automatically execute the logic written in the code, they also bring ethical challenges in terms of liability, specifically, deciding who is responsible in the event of incorrect executions [17]. Furthermore, at the expense of human ethics, blockchain and smart contracts could potentially be misused to enforce morally questionable government measures more efficiently [45]. Hence, with the expansion of blockchain applications to critical areas such as government services, there is an increasing need for comprehensive, standardized ethical frameworks that should be inspired by established ethics theories from related areas of information technology [54].

Blockchain transparency can be a double-edged sword, as many blockchain systems are public and permissionless but may not be understandable to end users and regulators [68]. Furthermore, transactions on public blockchains are typically pseudonymous, which can cause the user’s identity to be revealed, posing a risk to privacy [45,58]. For example, the integration of blockchain and multi-agent systems involves a trade-off between the immutability and transparency of blockchain, which strengthens trust between agents in the system, and privacy risks due to the potential disclosure of sensitive data from participating agents [66]. Privacy can be compromised by various techniques, such as behavioral analysis, traffic analysis, or linkability, where attackers examine data patterns in blockchain transactions to obtain sensitive information such as user identity, user habits, or user preferences [31]. Hence, blockchain raises ethical concerns by exposing sensitive information in some cases [28], requiring further privacy-enhancing cryptographic techniques [32], such as differential privacy or AI-based privacy-preserving techniques, for example, federated learning or homomorphic encryption [31]. In addition, careful design considerations are required to find a balance between efficiency, privacy, and fairness within the system [45]. As blockchain records are permanent and immutable, balancing efficiency, privacy, and fairness can result in conflicts with data protection regulations [58]. In healthcare, for example, blockchain stores sensitive health data permanently, making it difficult to correct errors retroactively [32]. Therefore, it is emphasized that privacy protection mechanisms, such as differentiated privacy, must be embedded into the design of the system. It should also be integrated across domains in order to achieve a holistic balance between system efficiency and human ethics [31].

Last but not least, some of the contributions that were included have implemented their concepts, most of them in the Ethereum ecosystem [44]. For example, the case involving organ donation and transplantation was implemented in an open-source private Ethereum blockchain-based solution that takes into account strict legal, ethical, and technical constraints [43]. Other concepts were implemented with permissioned blockchains, for example, the multi-agent system integrated with Hyperledger Fabric and the Java Agent Development Framework [66].

6. Framework Development

Based on the results of the quantitative and qualitative analyses of the respective previous sections, Section 4 and Section 5, a taxonomy is developed in this section. The themes, classifications, and patterns identified in the qualitative analysis serve as a basis for the taxonomy. The more detailed findings of the qualitative analysis are also incorporated into the taxonomy, which increases its accuracy and significance. The proposed taxonomy analyzes the investigated topic from three different perspectives: following the literature [17], we propose a micro, meso, and macro level for the investigation of blockchain-based AI alignment, which allows for technical, application, and societal perspectives.

Micro-, meso-, and macro-level analysis is an established approach to studying innovative technologies and their societal impact. In this regard, this approach has been used, for example, to analyze blockchain-related topics such as cryptocurrencies and business ethics [71], blockchain and the circular economy [72], and blockchain governance in the public sector [73]. In addition, this approach has been used to analyze AI in public administration [74] and in the circular economy [75] to provide policy recommendations on how to minimize the risks associated with the use of AI. The micro-, meso-, and macro-level analysis therefore helps to explain sociotechnical phenomena and shape social transformation processes. Our present research was inspired by these works and tailored to better understand the analysis topic and provide recommendations on how blockchain can facilitate safe and ethical behavior in AI agents. As mentioned above, these AI agents can have an increasing impact on society.

The proposed taxonomy is based on a meta-analysis of the systematically identified and analyzed literature. This taxonomy is deliberately chosen as an analytical framework because it offers a comprehensive and multifaceted perspective on the complex interplay between AI, blockchain, and ethics. The development of a taxonomy at the micro, meso, and macro levels serves as a structured approach to analyzing and presenting the heterogeneity of the included studies. By categorizing studies according to these levels, the overview systematically examines how ethical challenges and solutions manifest themselves at different levels, namely, at the technical, application-specific, and societal levels.

Specifically, the micro level highlights technical and operational characteristics, the meso level emphasizes domain-specific or sector-specific concerns, and the macro level addresses societal, regulatory, economic, and philosophical considerations. These three levels of analysis show how ethical challenges and solutions can arise and be addressed through code-level protocols, domain-specific applications, and system-wide governance and philosophy. The meta-analysis of the included studies shows that blockchain-based AI alignment requires attention at each of these levels, from the cryptographic primitives that secure user data to the domain-specific requirements of real-world use cases to universal moral principles and international standards and frameworks. The taxonomy is described below and illustrated in Figure 7.

6.1. Explanations of Decisions Involving Ethical Considerations

Ethics of Technology includes well-established Ethics Theories such as Virtue Ethics, Social Contract Theory, Kantianism, Rule Utilitarianism, and Act Utilitarianism [47,55]. These foundational philosophies offer different ways to evaluate actions for their appropriateness, the nature of moral duty, and the debate about ends versus means. They serve as a theoretical basis on which AI ethics guidelines can be built and evaluated [17]. Ethics of Technology also includes Emerging Technologies, which comprises AI, Big Data, Blockchain, Internet, and Machines and Robots [31], reflecting the ever-evolving technological landscape that poses ethical dilemmas. Those dilemmas could be about data privacy, algorithmic bias, and the social implications of widespread automation. By mapping ethical theories alongside these emerging technologies, established moral frameworks can guide and inform new ethical challenges [17,50].

Ethical concerns include the potential inequalities that might arise when powerful entities control too many intellectual resources or AI input data [61,63]. Conversely, well-designed blockchain solutions can empower marginalized communities by providing transparent financial services or secure identification [31]. Yet, ethical dilemmas appear around sustainability when large-scale blockchain usage requires substantial energy or specialized hardware [25,29]. Moreover, societies differ in how they prioritize privacy, security, and public interest, requiring adaptive frameworks that can accommodate these cultural, legal, and normative variations [65,68].

6.2. Application of the Proposed Framework

Building on the taxonomy described above, Figure 8 presents a visual summary of the sentiment polarity and subjectivity across the micro, meso, and macro levels of analysis. The chart plots sentiment polarity on the x-axis (with 0 indicating neutral sentiment) and subjectivity on the y-axis (with higher values indicating greater subjectivity). Each dot represents a study and is color-coded by its associated level. The overall distribution shows that most studies tend to express a mildly positive sentiment with moderate subjectivity. As this applies to the papers covering particularly the meso and micro levels, it reflects a cautious optimism grounded in technical or domain-specific analysis. In contrast, a few macro-level studies fall on the negative polarity side with higher subjectivity, potentially reflecting critiques related to societal, political, or environmental concerns. This trend suggests that micro- and meso-level discussions remain focused on pragmatic implementations and domain-based challenges. On the other hand, macro-level debates engage more normatively and critically. As a consequence, they have broader implications for blockchain-based AI governance.

The proposed framework is applied to the studies included in the SLR and presented in

Table 4. Proposed framework applied to included studies. Bold and Italic highlights are needed to quickly understand the dimension and main leaf.

Dimension	Main Leaf	Year	Reference	Description
Micro Level	Autonomy	2019	Ref. [62]	Explores blockchain integration within distributed AI and multi-agent systems.
	Consensus	2023	Ref. [11]	Technical paper on blockchain-based AI alignment using a Proof of Personhood consensus mechanism.
	Responsibility	2023	Ref. [33]	Develops a technical framework for ethics risk assessment.
		2022	Ref. [48]	Focuses on the organizational level of blockchain integration in corporate environments.
	Security	2023	Ref. [34]	Analyzes security, transparency, and governance aspects of Metaverse applications.
	Smart Contracts	2022	Ref. [49]	Analyzes the microeconomic effects of crypto-economic tokens on user behavior.
		2019	Ref. [66]	Analyzes blockchain infrastructure, smart contracts, and trust mechanisms.
	Transparency	2022	Ref. [44]	Explores AI-driven fake news detection using technical methods.
Meso Level	Codified Laws	2020	Ref. [57]	Examines blockchain’s role in corporate governance and sector-specific regulation.
	Financial Services	2023	Ref. [39]	Examines blockchain integration in accounting ERP systems, an industry-specific issue.
		2020	Ref. [58]	Focuses on the sector-specific impacts of blockchain in the financial industry.
	Healthcare	2024	Ref. [30]	Focuses on healthcare’s industry-specific regulations and challenges, which places it at the meso level.
		2024	Ref. [32]	Focuses on healthcare-specific regulations and practical use cases, positioning it within the meso-level discussion.
		2023	Ref. [35]	Discusses trustworthy AI in healthcare, an industry-specific application.
		2022	Ref. [43]	Discusses blockchain applications in a specific industry (healthcare).
		2021	Ref. [52]	Focuses on healthcare-specific blockchain applications and regulatory compliance.
		2021	Ref. [53]	Addresses healthcare-specific blockchain implementations and privacy challenges.
		2020	Ref. [56]	Focuses on blockchain-based informed consent management in the healthcare sector.
	Societal Services	2024	Ref. [25]	Focuses on organizational adoption and governance of blockchain technology.
		2023	Ref. [36]	Explores blockchain adoption in a specific industry (fruit supply chain).
		2022	Ref. [42]	Examines ethical sourcing challenges in a specific industry, making it a meso-level issue.
		2021	Ref. [51]	Focuses on the humanitarian sector, making it a meso-level issue.
Macro Level	Emerging Technologies	2024	Ref. [31]	Emphasizes the need for privacy considerations at a society-wide level, integrating legal frameworks, social equity, and long-term policy.
		2021	Ref. [50]	Examines large-scale ethical, legal, and governance considerations for AI.
		2020	Ref. [17]	Explores the governance, decentralization, and broad societal impacts of blockchain.
		2020	Ref. [54]	Provides a broad review of blockchain’s ethical and regulatory frameworks.
		2019	Ref. [67]	Explores blockchain, AI, IoT, and ethical governance at a societal level.
		2019	Ref. [61]	Discusses AI decentralization, governance, and ethical frameworks on a global scale.
		2019	Ref. [63]	Examines AI safety, governance, and regulatory frameworks for AGI development.
		2018	Ref. [68]	Develops a governance-oriented framework for blockchain’s impact on human interaction.
	Ethics Theories	2024	Ref. [27]	Although the article touches on some micro-level ledger details, it ultimately focuses on blockchain’s broad societal and regulatory impacts.
		2022	Ref. [47]	A broad philosophical examination of Kantian ethics in a global blockchain context, making it a macro-level analysis.
		2020	Ref. [55]	Discusses AI ethics, digital governance, and societal accountability on a broad level.
	Governance	2024	Ref. [26]	Focuses on system-wide, regulatory, and societal challenges in national infrastructure.
		2024	Ref. [28]	Centers on the large-scale, policy-relevant impacts of blockchain, spanning governance, regulation, and societal concerns.
		2024	Ref. [29]	Focuses on how blockchain can be integrated into biomedical research through broad regulatory and ethical norms.
		2023	Ref. [41]	Focuses on comprehensive government-driven standards for educational data ethics.
		2023	Ref. [40]	Emphasizes global adoption and international ethical frameworks for blockchain–digital twin technologies.
		2023	Ref. [37]	Focuses on AI and public governance, which aligns with macro-level policy concerns.
		2022	Ref. [45]	Discusses blockchain’s role in public governance and ethics at a macro scale.
		2020	Ref. [59]	Examines AI governance, transparency, and regulatory compliance at a societal level.
		2019	Ref. [60]	Focuses on AI transparency, governance, and compliance with global privacy laws.
		2019	Ref. [65]	Analyzes blockchain governance, decentralization, and economic impacts.
	Society	2023	Ref. [38]	Examines global ethics, identity, and data rights at the macro scale.
		2022	Ref. [46]	Discusses the societal and regulatory implications of fake news and blockchain at the macro level.
		2019	Ref. [64]	Focuses on global systemic risks and power imbalances in humanitarian tech use.

. The first column, Dimension, contains the levels covered in the considered selected studies. The Main Leaf column contains which branch the selected study covers. In the last column, Description, we explain how the main leaf branch is covered in the selected study. The columns Year and Reference provide additional information on the timeline of the research.

7. Discussion

This article presents an SLR that identifies key ethical themes at the intersection of AI and blockchain, and explores how features of blockchain technology can complement AI in addressing various challenges in current AI systems. As generative AI becomes increasingly pervasive in society, the need for robust ethical frameworks that prioritize human well-being and address concerns such as privacy and bias becomes paramount. Blockchain, with its emphasis on trust, transparency, and decentralized collaboration, can enhance the explainability and accountability of AI systems.

Our findings suggest a promising but complex landscape characterized by both opportunities and constraints. The analysis presented in this article demonstrates that blockchain can contribute most effectively to AI alignment through three primary avenues: transparency, immutability, and decentralized control. These qualities facilitate auditability and accountability in AI decision-making, which are critical to ensuring alignment with human values. Notably, smart contracts can be leveraged to codify ethical principles grounded in well-established ethical theories, such as Kant’s categorical imperative. Maintaining human control in future AI scenarios is vital, necessitating mechanisms such as Proof of Personhood and human-in-the-loop systems.

A key finding of this SLR is the importance of ethical considerations at multiple levels: the micro (technical), meso (application), and macro (societal) levels, all of which are essential for achieving blockchain-based AI alignment. These levels correspond to different stakeholders and systems, ranging from cryptographic primitives and consensus protocols to domain-specific applications (e.g., healthcare, education, and finance), as well as the overarching principles of justice and human dignity.

Use cases in healthcare, supply chains, education, and humanitarian services illustrate the tangible benefits of integrating blockchain with AI. For example, blockchain ensures data integrity in medical diagnostics, enables consent traceability in clinical trials, and protects sensitive student information in AI-powered educational systems. Across these domains, the blockchain layer serves as critical infrastructure for ethical enforcement and transparency, ultimately enhancing public trust.

Despite its potential, the integration of blockchain technology into AI alignment frameworks faces significant challenges. The identified limitations of any SLR are related to biases in the availability of publications and the study selection processes, inaccuracy in the study extraction process, and misclassification of published results. Additionally, we have identified the following challenges that provide exciting opportunities for future work: Our review reveals a gap in the literature, as much of the existing research overlooks or fails to address the emerging ethical implications of blockchain, highlighting an urgent need for comprehensive ethical frameworks and standards that address critical concerns such as accountability, transparency, and interoperability. On the technical side, issues like scalability and privacy risks demand the use of advanced cryptographic techniques and careful design. Although practical implementations demonstrate the potential of blockchain in supporting AI alignment, they also underscore the importance of overcoming existing barriers, as this is needed to further enable the development of ethical blockchain-based AI systems.

Research Agenda

Studies that focus on micro-level concerns generally examine how blockchain core features, such as consensus protocols, smart contracts, or data privacy methods, can enhance AI systems on a technical or organizational basis. At the meso level, different domains are examined. However, studies focusing on this level of analysis provide examples of blockchain, AI, and ethics, but rarely situate their findings in a wider integrative context, in particular, the context that could inform multi-domain applications or facilitate cross-sector interoperability. Studies that adopt a macro-level view highlighting themes like regulatory policy, societal acceptance, or overarching ethics theories often do not specify how to translate them into implementable global standards and frameworks. Consequently, there is a gap in the literature when it comes to designing and validating holistic frameworks that can connect micro-level technical architectures, meso-level domain-specific requirements, and macro-level policies in a unified way. The existing research rarely explains how micro-level tools, such as Zero-Knowledge Proofs, blockchain consensus mechanisms, or token-based governance, can be seamlessly linked to macro-level requirements, such as sustainability, human rights, and ethics. This is especially true in cross-sector AI applications where stakeholders may have heterogeneous goals and legal obligations. Moreover, only a few studies propose criteria or metrics for a systematic assessment of blockchain-based AI alignment. This leaves open questions on how to measure effectiveness, assess risks, and adapt to evolving use cases and regulations. To close this gap, we have identified and outline here the following research directions:

• Multi-Layered Framework Development: Integrated models should be developed that incorporate micro-level security and privacy mechanisms, meso-level sectoral governance, and macro-level legal constraints into a single, adaptable structure. Researchers could develop prototype architectures in which smart contracts or token systems reflect high-level ethical rules while maintaining sector-specific standards.
• Cross-Sector Interoperability: Future studies could systematically compare how blockchain-based AI systems work in sectors as diverse as healthcare and financial services to derive cross-sector best practices. This comparative perspective should clarify how universal ethical guidelines can be mapped to localized compliance requirements to enable cross-sector interoperability of technology and governance.
• Metrics and Evaluation for AI Alignment: Standardized metrics are needed to assess whether blockchain-based AI solutions meet ethical standards regarding, for example, privacy and fairness. Future work could investigate quantifiable indicators such as bias detection rates or transparency indices and evaluate them empirically in various pilot projects and industrial use cases.
• Adaptive Governance and Lifecycle Management: Another gap in the literature concerns the continuous monitoring of blockchain-based AI systems, where ethical requirements evolve in parallel with changing regulations or application-specific constraints. Research on adoptive governance mechanisms, smart contract updates, and automated monitoring would contribute to ensuring that, once deployed, solutions remain compatible with evolving societal expectations.
• Scalability and Sustainability: Only a few papers address the environmental footprint or scalability bottlenecks of emerging technologies such as blockchain and AI. Future work should therefore discuss efficient consensus algorithms, off-chain data approaches, and hardware optimizations so that blockchain-based AI alignment does not become too resource-intensive.

Addressing these research directions would assist in harmonizing technical innovations in blockchain-based AI governance, domain-specific applications, and broader ethical and legal frameworks, and thus transform the fragmented and localized approaches into a coherent ecosystem for blockchain-based AI alignment. In order to address the research gap described above, the following research questions are proposed. In a further step, the research questions are broken down and described per dimension in

Table 5. Proposed research agenda per dimension. Bold and italic highlight is needed to immediately spot the dimension and leafs.

Dimension	Leafs	Selected Ethical Consideration and Challenges	Research Questions
Micro	Autonomy; Consensus; Interoperability; Responsibility; Robustness; Security; Smart Contracts; Transparency.	Accountability, bias, and fairness; Privacy vs. transparency; Scalability vs. decentralization; Secure code and infrastructure.	How can blockchain-based smart contracts enforce compliance with ethical principles in AI systems and offer flexibility to enforce dynamic adjustments? How can blockchain enhance the transparency and explainability of AI models to mitigate algorithmic bias? How can blockchain-based cryptographic methods ensure robust data privacy in AI training and inference processes? How can decentralized consensus mechanisms contribute to maintaining human oversight and ethical controllability in AI systems? How can blockchain enforce accountability in the autonomous decision-making of AI systems?
Meso	Automated Driving; Codified Laws; Financial Services; and Cryptocurrencies; Healthcare; Legal Services; Societal Services; Supply Chains.	Data sovereignty and consent; Diverse stakeholders (companies, regulators, citizens); Innovation vs. user protection; Sector-specific ethical guidelines; Token distribution and auditing.	How can blockchain improve ethical data management and interoperability standards for healthcare AI systems? How can blockchain address fairness and ethical compliance in AI applications for financial services? How can blockchain-based traceability frameworks ensure accountability in supply chains that utilize autonomous vehicles? How can blockchain facilitate ethical oversight and governance in automated driving systems? How can blockchain-based smart contracts support the automated enforcement of sector-specific ethical and regulatory compliance for AI systems?
Macro	AI; Big Data; Blockchain; Internet; Machines and Robots.	Centralization of power; Diverse international regulations; Socioeconomic impacts; Sustainability; Translating classical ethics into code.	How must global regulatory frameworks be designed to harmonize blockchain-based AI alignment and ethics across different cultures and jurisdictions? How can ethics theories, such as Kantianism, influence blockchain-based AI governance structures on a global scale? How must blockchain-based token incentive mechanisms be designed to prevent power concentration and promote equitable governance in AI systems? How must governance strategies be designed to ensure the long-term resilience and ethical alignment of blockchain-based AI systems in society? How can the integration of blockchain and AI achieve a balance between environmental sustainability, ethical obligations, security, and performance?

using the taxonomy developed in this work.

8. Conclusions

This systematic literature review (SLR), through comprehensive examination, identification, inclusion, extraction, evaluation, synthesis, and detailed analysis of 46 relevant studies, identified a gap at the intersection of artificial intelligence (AI) alignment, blockchain, and ethics. The taxonomy developed in this SLR reveals that aligning AI with human values is not solely a technological endeavor; it also intersects with legal, social, ethical, and economic dimensions. The findings of this paper describe how these areas need to interact to ensure that AI developments remain within ethical boundaries, which can be enabled through integration with blockchain and other emerging technologies. For this purpose, the intersection of blockchain and AI presents a powerful opportunity to address the ethical challenges associated with increasingly sophisticated AI and a wider range of applications. By leveraging blockchain’s inherent attributes, such as transparency, immutability, auto-enforceability, and new forms of governance, an AI alignment framework can be created that prioritizes human values and societal well-being in AI development and deployment processes. The convergence of blockchain and AI offers not only technical solutions to complex ethical dilemmas but also fosters a collaborative and participatory approach to AI governance, ensuring that powerful AI models benefit all.

As AI continues to evolve, this paper offers valuable insights for navigating the ethical landscape and building a future where humanity and AI coexist in harmony with the support of blockchain technology. Thus, this research identified several directions of future work that include multi-layered framework development, ensuring cross-sector interoperability, developing metrics and evaluation for AI alignment, empowering adaptive governance and lifecycle management, and establishing scalability and sustainability. The research questions that coincide with the developed taxonomy and address the research directions were developed and broken down along each dimension of the developed taxonomy.