Call for papers/Topics

Full Articles/ Reviews/ Shorts Papers/ Abstracts are welcomed in the following research fields:

Foundational & Independent Challenges

These areas focus on the distinct technical, ethical, and immediate operational bottlenecks inherent to the development and deployment of AI systems.

1. Technical Risks and Algorithmic Vulnerabilities

The inherent failures and security gaps built directly into AI architectures and data pipelines.

  • The "Black Box" Problem: Lack of interpretability and explainability in deep neural networks.

  • Hallucination and Error Propagation: The tendency of large models to confidently generate false, unverified information.

  • Adversarial Exploitation: Vulnerabilities to prompt injection, data poisoning, and model inversion attacks.

  • Data Quality and Scarcity: Managing biased, missing, or unrepresentative datasets that degrade model accuracy.

2. Ethical and Algorithmic Governance

The fundamental human rights, fairness, and accountability dilemmas posed by autonomous code.

  • Algorithmic Bias and Discrimination: Implicit and explicit biases in training data leading to discriminatory outcomes in hiring, lending, or law enforcement.

  • Plagiarism and Intellectual Property: Unauthorized scraping of copyrighted data to train proprietary foundation models.

  • Autonomous Agency and Legal Liability: Determining accountability (developer vs. user vs. manufacturer) when an autonomous system causes physical or financial harm.

  • Privacy and Surveillance Overreach: Infringement of individual data rights via mass biometric tracking and non-consensual personal profiling.

3. Resource and Environmental Sustainability

The physical constraints and material footprint required to power digital intelligence.

  • Energy Demand and Carbon Footprint: Extreme power consumption of hyperscale data centers needed for training and inference.

  • Water Consumption: High volumes of fresh water required to cool high-performance AI compute hardware.

  • Electronic Waste (E-Waste): Rapid hardware obsolescence cycles for advanced processing units (GPUs and TPUs).

Interrelated & Systemic Impacts

These fields represent the massive, overlapping consequences where AI intersects with economics, society, information ecosystems, and global security.

1. Labor Markets and Economic Stratification

The disruptive convergence of corporate automation, changing skill requirements, and wealth distribution.

  • Job Displacement vs. Augmentation: The replacement of routine cognitive and manual labor versus the creation of human-in-the-loop hybrid roles.

  • The AI Divide: Widening economic inequalities between tech-monopoly nations and developing economies with weak digital infrastructure.

  • Skill Devaluation and Cognitive Dependence: The atrophy of human critical thinking and technical skills due to over-reliance on automated tools.

2. Information Integrity and Societal Cohesion

The space where advanced generative capabilities reshape human communication, trust, and psychological safety.

  • Synthetic Media and Deepfakes: The proliferation of ultra-realistic audio and video used for targeted defamation, fraud, and political subversion.

  • Algorithmic Echo Chambers: Recommendation engines optimized for engagement that accidentally accelerate radicalization and public polarization.

  • Mass-Produced Disinformation: The low-cost scaling of persuasive, fabricated news designed to destabilize public trust in democratic systems.

3. Geopolitics, Warfare, and Global Security

The intersection of state power, defense strategy, and the race for technological supremacy.

  • Lethal Autonomous Weapons Systems (LAWS): Drones and robotic systems authorized to select and engage targets without direct human intervention.

  • The AGI Hegemony Race: Geopolitical competition between major global powers to achieve Artificial General Intelligence first, upsetting the balance of power.

  • Asymmetric Cyber Warfare: Automated vulnerability discovery and AI-driven social engineering attacks launched by state-sponsored actors.

4. Sociotechnical Transformation of Public Sectors

How the structural integration of AI alters the fundamental delivery of healthcare, education, and justice.

  • Automated Judicial Bias: Risk of systemic bias in predictive sentencing software and automated risk assessments.

  • Diagnostic and Clinical Over-reliance: Medical errors resulting from physicians relying too heavily on algorithmic triage and diagnostic software.

  • Plagiarism and Educational Redesign: The breakdown of traditional assessment methods in academia, forcing a total overhaul of pedagogy and grading