Focus and Scope

JAIG: Journal of Artificial Intelligence Governance is committed to publishing high-quality scholarly articles that examine the governance, regulation, accountability, and responsible development of artificial intelligence systems. The journal prioritizes research that demonstrates significance, novelty, methodological rigor, and practical relevance in addressing the ethical, legal, social, institutional, and technical challenges of AI implementation. Through an independent peer-review process, JAIG ensures critical and constructive evaluation to support academic excellence and meaningful contribution to the development of responsible and trustworthy AI governance.

JAIG serves as an interdisciplinary academic platform for scholars, policymakers, practitioners, regulators, technology developers, graduate students, and researchers who are interested in understanding how artificial intelligence can be designed, deployed, monitored, audited, and evaluated in accordance with ethical principles, regulatory requirements, public values, and institutional responsibilities.

Our Focus and Scope

JAIG aims to become a dynamic intellectual and research hub in the field of artificial intelligence governance. The journal welcomes original research articles, systematic literature reviews, conceptual papers, case studies, policy analyses, and methodological contributions related to the governance and responsible implementation of artificial intelligence. The focus areas include:

  1. Responsible Artificial Intelligence: Research on the ethical, transparent, inclusive, and socially responsible design, development, and deployment of AI systems.
  2. AI Ethics and Moral Accountability: Studies examining ethical principles, moral responsibility, value alignment, bias mitigation, and the societal consequences of AI-driven decision-making.
  3. Algorithmic Accountability: Research focusing on accountability mechanisms, auditability, responsibility attribution, algorithmic impact assessment, and oversight of automated systems.
  4. AI Regulation and Legal Frameworks: Studies on AI laws, regulatory models, compliance requirements, legal liability, data protection, and policy responses to AI development.
  5. Trustworthy and Explainable AI: Research on explainability, interpretability, transparency, reliability, robustness, fairness, and user trust in artificial intelligence systems.
  6. Risk-Based AI Governance: Studies addressing AI risk classification, risk assessment, mitigation strategies, safety controls, monitoring frameworks, and governance models for high-risk AI applications.
  7. Human-Centered AI Governance: Research exploring human oversight, user autonomy, human-AI interaction, participatory design, social acceptance, and the protection of human rights in AI systems.
  8. AI Safety and Reliability: Studies examining system safety, robustness, failure prevention, model monitoring, security vulnerabilities, and safeguards for responsible AI deployment.
  9. Data Governance for AI: Research on data quality, privacy, consent, data stewardship, data lifecycle management, data ethics, and governance mechanisms supporting AI development.
  10. Public-Sector AI Governance: Studies on the adoption, regulation, evaluation, and accountability of AI systems in government, public services, smart cities, and digital public administration.
  11. Enterprise AI Governance: Research on organizational AI policies, compliance structures, internal control, AI risk management, governance maturity, and responsible AI implementation in business environments.
  12. Socio-Technical Implications of AI: Studies examining the broader impact of AI on society, institutions, labor, culture, inequality, public trust, and democratic governance.
  13. AI Policy and Digital Governance: Research on national AI strategies, digital transformation policy, platform governance, international AI governance, and institutional coordination in the digital ecosystem.
  14. AI Auditing and Compliance: Studies proposing or evaluating audit frameworks, compliance models, governance indicators, assessment tools, and practical mechanisms for monitoring AI systems.
  15. Fairness, Bias, and Inclusion in AI: Research addressing discriminatory outcomes, fairness metrics, inclusive system design, bias detection, bias mitigation, and equitable access to AI technologies.
  16. Institutional Responsibility and AI Governance Frameworks: Studies that connect technical AI development with governance structures, organizational responsibility, institutional readiness, and real-world implementation of AI governance principles.

The focus and scope of JAIG ensures that published research contributes to the advancement of artificial intelligence governance as both a technical and socio-institutional field. The journal encourages interdisciplinary contributions from computer science, information systems, law, public policy, management, social sciences, philosophy, ethics, and related disciplines.

JAIG particularly emphasizes research that bridges AI innovation with governance mechanisms, including transparency, fairness, accountability, privacy, compliance, auditability, safety, and institutional responsibility. Submitted manuscripts are expected to present clear research problems, relevant literature, rigorous methods, strong analytical discussion, and meaningful contributions to the responsible development and governance of artificial intelligence in real-world environments.