Nusantara Journal of Trustworthy Artificial Intelligence (NJTAI) is an international, peer-reviewed, and open-access scholarly journal that publishes conceptual, methodological, empirical, and applied research on the development, evaluation, governance, and implementation of artificial intelligence that is safe, responsible, transparent, fair, robust, privacy-preserving, explainable, accountable, and human-centered.

NJTAI gives particular attention to Indonesia, the Global South, multilingual societies, low-resource languages, local datasets, and environments with limited data and computing resources. The journal welcomes interdisciplinary studies connecting technical AI innovation with ethical, legal, social, economic, cultural, environmental, and organizational considerations.

NJTAI particularly encourages trustworthy AI research in strategic sectors, including healthcare, natural resources, environmental sustainability, logistics, and supply chain systems. Every submission must demonstrate a clear contribution to at least one dimension of trustworthy AI, such as safety, robustness, explainability, interpretability, fairness, privacy, transparency, accountability, human oversight, reproducibility, sustainability, or AI governance.

The journal welcomes manuscripts in, but is not limited to, the following areas:

1. Responsible, Trustworthy, and Human-Centered AI
   Responsible AI, trustworthy AI, ethical AI, human-centered AI, human oversight, value-sensitive design, participatory AI, and AI designed for human and public benefit.

2. AI Safety, Reliability, and Robustness
   AI safety, model reliability, adversarial robustness, uncertainty estimation, out-of-distribution detection, anomaly detection, fail-safe mechanisms, risk mitigation, and failure prevention.

3. Explainable and Interpretable Artificial Intelligence
   Explainable AI, interpretable machine learning, causal and counterfactual explanations, visual explanations, global and local interpretation, and evaluation of explanation quality.

4. Fairness, Bias, Transparency, and Algorithmic Accountability
   Algorithmic fairness, bias detection and mitigation, transparency-by-design, algorithmic auditing, documentation, accountability, and impact assessment of automated decisions.

5. Privacy, Security, and Privacy-Preserving AI
   Federated learning, differential privacy, secure multiparty computation, encrypted learning, anonymization, privacy-preserving analytics, AI security, and attacks against AI systems.

6. AI Governance, Ethics, Policy, and Regulation
   AI governance, ethics, public policy, regulation, standards, compliance, risk management, conformity assessment, audit trails, model cards, datasheets, and lifecycle governance.

7. Generative AI and Foundation Models
   Generative AI, large language models, foundation models, multimodal AI, vision-language models, agentic AI, alignment, hallucination detection, red teaming, prompt evaluation, and model safety.

8. Inclusive and Low-Resource Artificial Intelligence
   Natural language processing, speech processing, computer vision, and multimodal learning for Indonesian, regional languages, low-resource languages, vulnerable groups, and underrepresented communities.

9. Trustworthy Machine Learning and Intelligent Systems
   Machine learning, deep learning, computer vision, natural language processing, speech recognition, reinforcement learning, decision-support systems, and intelligent systems that explicitly address trustworthiness.

10. AI Engineering, MLOps, and Model Assurance
    AI software engineering, MLOps, continuous evaluation, verification and validation, model testing, monitoring, drift detection, incident management, quality assurance, and technical documentation.

11. Data Quality and Responsible Data Governance
    Data quality, local datasets, data governance, provenance, lineage, documentation, synthetic data, data-centric AI, representativeness, and data-bias mitigation.

12. Reproducible and Evidence-Based AI
    Reproducible AI, open science, benchmark development, empirical evaluation, ablation studies, replication studies, negative results, model comparison, robustness evaluation, and failure analysis.

13. AI for Indonesian Languages and Local Knowledge
    Datasets, models, benchmarks, and AI systems for Indonesian and regional languages, local knowledge, culture, traditional manuscripts, and preservation of Nusantara cultural heritage.

14. Trustworthy AI for Health and Healthcare Informatics
    Safe, explainable, fair, privacy-preserving, and human-centered AI for medicine, public health, hospitals, mental health, pharmacy, bioinformatics, medical imaging, disease detection, clinical prediction, electronic health records, personalized medicine, patient monitoring, clinical validation, and patient safety.

15. Trustworthy AI for Natural Resources and Environmental Sustainability
    Responsible AI for agriculture, forestry, marine and fisheries, mining, energy, water, biodiversity, climate, remote sensing, geospatial intelligence, land-use and land-cover analysis, disaster prediction, environmental risk assessment, and green AI.

16. Trustworthy AI for Logistics and Supply Chain Systems
    Transparent, reliable, safe, and accountable AI for demand forecasting, inventory, warehousing, routing, fleet management, last-mile delivery, cold chains, procurement, ports, maritime logistics, supply-chain risk and resilience, humanitarian logistics, traceability, digital twins, and decision support.

17. AI of Things, Edge AI, Robotics, and Autonomous Systems
    AI of Things, edge and embedded AI, smart cities, robotics, intelligent transportation, cyber-physical systems, digital twins, autonomous systems, and sensor-based intelligence with safety and human oversight.

18. Human–AI Interaction and Societal Impact
    Human–AI interaction and collaboration, trust calibration, user acceptance, algorithmic literacy, end-user explanations, social and economic effects, future of work, digital inequality, cultural impacts, sustainability, and environmental footprints.

Out-of-Scope Statement

A manuscript that merely applies a machine-learning or deep-learning model to a dataset without substantively addressing trustworthiness, safety, explainability, fairness, privacy, accountability, robustness, human oversight, reproducibility, sustainability, or responsible societal impact is outside the scope of NJTAI.

Article Types

NJTAI publishes original research articles, review articles, systematic literature reviews, methodological articles, dataset and benchmark articles, replication studies, negative-results and failure-analysis articles, policy and governance analyses, responsible-AI case studies, and short communications.