Mindfulness and self-efficacy in improving moral judgement under Artificial Intelligence (AI)

Businesses are undergoing a transformation due to AI technologies, which are adopting decision-making processes, improving efficiency, and expanding production in many areas such as medical care, banking, higher education, and government. While there are many benefits to AI-driven solutions, many are concerned about the ethical implications. We need to take a close look at the processes involved in creating, implementing, and maintaining AI systems because of serious problems such algorithmic biases, privacy concerns, moral obligation, and responsibility gaps. Because AI is increasingly impacting high-stakes decision-making, ensuring their ethical implementation requires a deeper understanding of the human values as well as psychological components of ethical leadership, as well as more than just technological interventions. Human considerations of ethical AI governance receive scant attention, despite the fact that AI fairness and interpretability are well-known subjects. Few studies have examined the cognitive and psychological aspects impacting ethical decision-making, despite the fact that many have focused on building algorithmic safeguards and legal frameworks [1]–[4].

Figure 1: Spiritual Intelligence Incorporates the Whole S.E.L.F (Spiritual Emotional Leadership Focus)[5]

Human-centered ethical intelligent governance forces leaders and decision-makers to strike a compromise between ethical issues and technological development. In this regard, ethical leadership in artificial intelligence governance is much influenced by psychological elements including self-efficacy and spiritual intelligence (SQ).One key but often disregarded component of ethical decision-making is spiritual intelligence (SQ.) It is defined as the ability of one to apply morality, values, and more general purpose to their decisions therefore creating moral integrity, empathy, and wisdom. Unlike cognitive intelligence ( IQ) or emotional intelligence ( EQ), SQ stresses on long-term vision, significant ethical awareness, and the capacity to go beyond personal preconceptions. When deciding on artificial intelligence, high SQ leaders are more likely to give human well-being, justice, and fairness first priority; so, the chance of immoral AI methods is reduced. Through spiritual intelligence, one also enhances moral responsibility and self-reflection, therefore ensuring that artificial intelligence systems complement humanistic values rather than just driven by profit

On the other hand, self-efficacy is a psychological trait explaining a person's confidence in their ability to make thoughtful and smart decisions. Self-efficacy impacts how decision-makers confront moral conundrums, resist outside pressure, and promote transparent and fair artificial intelligence policy inside the framework of artificial intelligence ethics. In research, high self-efficacy individuals have shown to be more proactive, resilient, and conscientious in managing challenging moral conundrums. Their more moral bravery enables them to resist unethical AI use, protect user privacy, and apply governance structures ensuring justice and openness in algorithms. The junction of spirituality with self-efficacy is a fundamental but seldom investigated junction in artificial intelligent decision-making. While SQ raises ethical awareness as well as moral reasoning, self-efficacy guarantees that ethical values are fully included into appropriate AI policies and practices. These kind of leaders are more equipped to balance ethical responsibility with technological efficiency, so ensuring consistent, inclusive, and favourable results of AI application for society [6]–[10].

The present work examines how mental flexibility and confidence affect AI ethics by reviewing current studies. It emphasises the need for an integrated AI ethical strategy that combines human-centered leadership and technological protection. The study bridges ethical theory of computational intelligence and practical decision-making to provide insightful analysis for legislators, corporate executives, and AI researchers working to develop socially responsible AI ecosystems.

A. Ethical Challenges in AI Implementation

AI-driven decision-making has prompted problems of bias, transparency, or responsibility due to research revealing how AI models can mirror society's prejudices, resulting in unfavourable outcomes in hiring, lending, and criminal justice (Challenges, 2024) [11]. Generational artificial intelligence systems have raised ethical concerns about privacy, data protection, infringement of copyright, misunderstandings, prejudices, and social inequality, notwithstanding its revolutionary nature. Generative AI's ability to create deepfakes and synthetic media undermines truth and trust, complicating ethical issues. (Ryan et al., 2024) [12]. As such, frameworks like the EU’s AI Act and IEEE’s Ethically Aligned Design stress the need for human oversight and moral reasoning to mitigate these issues. The interdisciplinary study by Ryan et al. (2024) reveals that structural AI ethics challenges, particularly those beyond individual control, require a more expansive view of ethics that extends beyond micro-level considerations to address broader systemic issues. The authors argue that AI ethics should not solely focus on the responsibility of developers, urging the inclusion of diverse stakeholders, such as quadruple helix participants, to create more holistic, multi-level, and interdisciplinary approaches to AI governance. This paper calls for a proactive stance in developing ethical AI systems, advocating for guidelines that prioritize human rights, fairness, and transparency (Challenges, 2024; Ryan et al., 2024).

Figure 2: Ethical issues in AI[12]

B. Spiritual Intelligence and Ethical Decision-Making

According to research, executives who score high on the SQ scale are more inclined to put the needs of people first, particularly when it comes to decisions involving AI. This means that moral considerations will trump financial ones. Ethical leadership requires spiritual intelligence (SQ) since it promotes attention, compassion, and integrity.[13]. Making ethical business decisions is crucial, as unethical activity, such as corporate scandals and fraud, has resulted in a staggering global loss of over $7.1 trillion. Based on Fry's idea of spiritual leadership, Cooper (2023) studied 102 American middle managers to determine whether there was a correlation between psychological leadership, spiritual health, and moral judgment. Being the only significant predictor of ethical behavior, the results highlighted the importance of incorporating hope and faith into managerial methods to guide moral decisions. In a broader sense, the research of also delves into the impact of SQ on decision-making.[14] Spiritual intelligence has a substantial impact on different decision-making styles, according to Ku and Samanta (2022). Through the use of structural equation modeling (SEM), their study shown that increasing professionals' spiritual awareness can lead to better decision-making, less stress, and ultimately greater ethical achievements. In order to promote ethical standards in business and AI development, our findings highlight the significance of incorporating spiritual intelligence into decisions and leadership processes.(Cooper, 2023; Ku & Samanta, 2022).

C. Self-Efficacy and Ethical Leadership

The degree to which individuals believe in their own abilities significantly impacts how they handle moral dilemmas related to AI policymaking. In the face of challenges related to artificial intelligence ethics, leaders with high self-efficacy tend to take a more proactive and resilient stance. This makes them more responsible for their actions and better able to withstand unethical pressures that aim to advance AI development. Confidence in one's ability to make moral decisions and preserve integrity under duress improves one's capacity to do so (Santiago-Torner & Jiménez-Pérez, 2025). Developing a theoretical framework [15] demonstrate how moral leadership creates strong bonds—which are quite necessary for an environment suited for group learning. This point of view stresses the need of morally based ethical systems in avoiding ethically motivated circumstances led by selfishness and in improving self-efficacy, unlike just promoting individualistic traits. 2022: Uppathampracha and LiU [16] looked into how creative job performance, self-efficacy, and ethical leadership interacted. Their study revealed, using structural equation modelling, or SEM, and data collected from 441 workers at banks in Thailand, that a sense of self-worth moderates the link between ethical leadership as well as creative output. According to Uppathampracha and Liu (2022), employment engagement moderates this connection sequentially and that ethical leadership and creative activity need self-efficacy. These research stress the need of self-efficacy in developing ethical decisions and in being a good corporate participant (Santiago-Torner & Jiménez-Pérez, 2025; Uppathampracha & Liu, 2022).

Using secondary data analysis, this paper synthesises policy reports, academic papers, and AI ethics frameworks to investigate the part spiritual intelligence and self-efficacy play in ethical artificial intelligence governance. This strategy guarantees a thorough assessment of AI governance issues by using qualitative research techniques, therefore eliminating prejudices associated to main data collecting.

Figure 3: Proposed Flowchart

D. Research Design and Justification

Conceptual systems, case studies, ethical questions of artificial intelligence governance were investigated using a qualitative study method. This design ensures a thorough understanding of how spirituality and self-efficacy promote ethical decision-making by letting a deep evaluation of past studies. The study guarantees credibility and objectivity by using a methodical assessment of secondary sources, therefore reducing the prejudices connected with primary data collecting.

Emphasising conceptual models, case studies, and ethical issues in AI governance, a qualitative research methodology was chosen. This methodical review of previous research reduces biases usually connected with primary data collecting and increases the validity of results.

E. Data Collection Sources

The study depended on a wide spectrum of secondary sources, including:

·                     Academic Journals: Peer-reviewed works from databases like Elsevier, IEEE Xplore, and Springer.

·                     Policy Reports: Ethical policies and AI governance structures from organizations include the European Union (EU), UNESCO, and IEEE.

·                     Books and White Papers: Works on the junction of ethics, artificial intelligence, and human cognition.

·                     Case Studies: Real-world applications of ethical AI principles in corporate and governmental decision-making.

F. Data Analysis Techniques

A theme study was carried out to combine ideas from different sources. This engaged:

1.                 Identifying Key Themes: recurring ideas on ethical AI governance, self-efficacy, and spiritual intelligence.

2.                 Comparative Analysis: Analyzing how various frameworks handle artificial intelligence decision-making tests difficulties.

3.                 Triangulation: Cross-referencing results from several sources guarantees dependability and helps to decrease prejudice.

G. Strengths and Limitations

Strengths:

·                     Broad Perspective: Availability of thorough study results free from primary data collecting limitations.

·                     High Reliability: Use of peer-reviewed sources enhances the credibility of the study.

·                     Time-Efficient: Allows for rapid data synthesis compared to conducting surveys or interviews.

Limitations:

·                     Lack of Real-Time Data: Findings may not fully reflect emerging trends in AI ethics.

·                     Dependence on Available Literature: Insights are limited by the scope and quality of previously published research.

·                     No Direct Participant Involvement: The study lacks first-hand perspectives from AI practitioners and policymakers.

H. Ethical Considerations

Since this study relies solely on secondary sources that are publicly available, there were no ethical concerns regarding participant privacy, informed consent, or data confidentiality. Nevertheless, we did our best to provide an unbiased account of the results and properly cite all sources.

Privacy, informed permission, or confidentiality are not issues as this study is totally dependent on secondary sources. The correct referencing guarantees an objective and moral attitude.

By adopting secondary data analysis and integrating thematic and comparative analysis, this study offers new insights into AI governance through the lens of spiritual intelligence and self-efficacy. The inclusion of global AI ethics frameworks, decision-making models, and leadership theories highlights a novel interdisciplinary approach that enhances AI ethics governance and leadership practices.

A. The Role of Spiritual Intelligence in AI Ethics

The findings reveal that spirituality (SQ) makes artificial intelligence (AI) more sensitive and ethically resilient, which in turn influences AI decisions by reason and fairness. Good SQ leaders have better ethical vision, which means there is less need to worry about bias in algorithms, privacy invasions, and AI abuse. High SQ leaders provide values, compassion, and a feeling of purpose to AI governance by helping confidence along with responsibility in AI-driven systems.
Deep introspection motivated by spiritual intelligence helps artificial intelligence professionals identify and fix their moral blind spots. It guarantees that AI uses match larger social benefit instead of only temporary financial benefits and fosters connection. Research shows that individuals with high SQ are better able to empathize, consider other perspectives, and take the initiative when faced with ethical dilemmas involving AI. As a result, they are in favor of a governance strategy for AI that prioritizes humans.

B. Self-Efficacy as a Catalyst for Ethical AI Governance

Self-efficacy is the belief of a person on their ability to apply suitable artificial intelligence techniques and negotiate challenging moral dilemmas. Professionals with high self-efficacy in artificial intelligence are more likely to oppose demands to break ethical standards, support justice, and challenge unethical AI methods. Studies on higher self-efficacy have connected them to better leadership, more accountability, and proactive ethical artificial intelligence governance decision-making.

Having faith in one's own abilities helps people apply AI ethically, even in high-pressure situations when budgetary and operational limitations could lead to moral dilemmas. The ability to adapt and bounce back is fostered, which in turn helps AI developers find solutions that balance technological progress with ethical considerations. Additionally, advocates for ethical AI are more likely to have high levels of self-efficacy, which should encourage governments, corporations, and AI developers to embrace AI models that prioritize transparency, accountability, and fairness.

C. Integrating Spiritual Intelligence and Self-Efficacy in AI Governance

Organizations have to aggressively include SQ and self-efficacy training in AI leadership initiatives to advance artificial intelligence ethics. Crucially, developing training programs that improve ethical awareness, moral judgment, and responsible artificial intelligence governance raises awareness of Emphasizing a human-centered approach, artificial intelligence ethics rules should make sure that technology complements moral responsibility, societal good, and fairness. Establishing organized ethical leadership training whereby experts learn how to apply SQ and self-efficacy in artificial intelligence decision-making is one strategy. Case-based ethical simulations let companies help AI executives to practice value-driven ethical dilemma resolution. Improving ethical foresight and leadership capacity is cross-disciplinary cooperation among creators of artificial intelligence, ethicists, legislators, and spiritual intelligence experts. Organizations can enhance AI ethics governance systems by encouraging a culture that values ethical thought and confident decision-making. As a result of this all-encompassing plan, artificial intelligence systems will always follow basic moral guidelines. As a result, the dangers of AI prejudice, data exploitation, and immoral automation techniques will be lessened.

Table 1: Comparison of Proposed Work with Existing Studies on AI Ethics

This paper closes a significant void in studyethical AI leadershipstudy by including studyspiritual intelligence (SQ) and self-efficacy (SE) into AI governancestudy, a field mainly neglected in earlier studies. This study adopts a studycomprehensive, psychology-driven approachstudy to support ethical AI decision-making unlike other studies that concentrate just on studypolicy frameworksstudy or studyspirituality in leadershipstudy. Promoting studySQ and SEstudy training for studyAI professionalsstudy improves studymoral reasoning, openness, and responsibilitystudy in artificial intelligence ethics. Strengthening studyhuman-centered AI governancestudy, this studyinterdisciplinary perspectivestudy makes studymore resilient, equitable, and socially responsiblestudy, so defining a new benchmark in studyethical AI leadership and decision-makingstudy.

The need of mindfulness, self-efficacy, and spiritual intelligence in forming moral judgement, responsible decision-making, and accountability in AI governance is underlined in this paper. Although debates on artificial intelligence ethics may centre on technological and legal frameworks, the psychological factors impacting ethical decision-making are still underreselled. This article shows how mindfulness improves attention, self-efficacy promotes resilience, and spiritual intelligence supports integrity in AI-driven decision-making by synthesising peer-reviewed literature, AI ethical frameworks, and leadership studies.Results show that including these psychological elements into AI ethics training courses can help experts in artificial intelligence increase ethical compliance, openness, and decision-making capacity. While self-efficacy enables AI leaders to make confident and appropriate decisions, mindfulness helps them to clearly manage ethical challenges.Future studies examining cross-cultural differences in mindfulness, self-efficacy, and spiritual intelligence will help to create internationally flexible AI policy suggestions. Including several AI ethics models, such the EU AI Act and IEEE Ethically Aligned Design, will also enable a more all-encompassing and adaptable ethical governance structure. Applying real-world artificial intelligence and varied datasets will help to validate these discoveries in many other fields.To foster ethical resilience and human-centered AI leadership, companies should create organised training courses including mindfulness techniques, self-efficacy development, and spiritual intelligence including By focussing on these topics, AI governance plans will be strengthened and responsibility, openness, and justice guaranteed in AI-driven decision-making in many different sectors.