All Abstracts, Reviews, short articles, Full articles, Posters are welcomed related with any of the following research fields:
These areas focus on distinct, specific categories of friction and change brought about by AI.
The inherent technical limitations, engineering hurdles, and security flaws within current AI architectures.
The Black Box Problem: Lack of interpretability and explainability in deep neural networks, making it difficult to understand how an AI reached a specific conclusion.
Data Hallucinations and Errors: The tendency of Large Language Models (LLMs) and generative systems to confidently fabricate false information.
Adversarial Vulnerabilities: The susceptibility of machine learning models to "adversarial attacks," where subtle, malicious tweaks to input data completely trick the AI.
Brittleness and Edge Cases: The failure of AI systems when encountering real-world scenarios that fall slightly outside their training data distribution.
Compute and Energy Scalability: The massive environmental and infrastructure costs required to train and run frontier models.
The moral dilemmas and systematic biases embedded directly within AI systems and data pipelines.
Algorithmic Bias and Discrimination: AI replicating or amplifying historical human biases found in training data, leading to unfair outcomes in hiring, lending, and policing.
Privacy and Data Extraction: The mass harvesting of personal data without explicit consent or fair compensation to train foundational models.
Copyright and Intellectual Property Infringement: The legal and ethical grey area of training generative AI on copyrighted art, text, and proprietary code.
Synthetic Media and Deception: The weaponization of hyper-realistic deepfakes, voice cloning, and automated disinformation campaigns at scale.
Alignment Problem: The fundamental challenge of ensuring that highly advanced AI systems act in accordance with human values and intent.
How the deployment of AI fundamentally reshapes human structures, institutions, and daily life.
Labor Displacement and Job Automation: The restructuring of the workforce as cognitive and creative tasks are automated, leading to economic transition anxiety.
The Digital and Economic Divide: The concentration of AI power and wealth within a few massive tech monopolies and wealthy nations, widening global inequality.
Cognitive Atrophy and Reliance: Human over-reliance on automated systems, potentially degrading critical thinking, writing, and problem-solving skills over time.
Social Isolation and Echo Chambers: The rise of emotionally simulating AI companions and hyper-personalized algorithmic feeds that detach individuals from real-world communities.
These fields represent the critical intersections where technical, ethical, and societal issues blur together, requiring multi-disciplinary solutions.
The intersection of technical capabilities, legal frameworks, and international state competition.
The Pace Law Gap: The challenge of drafting agile legal frameworks (like the EU AI Act or global treaties) that can keep up with the exponential velocity of AI development.
Sovereign AI and Geopolitical Tech Races: Nation-states competing for semiconductor supply chains, data centers, and AI dominance, treating AI as a core component of national security.
Corporate Monopolization vs. Open Source: The intense debate between locking down AI models behind corporate API walls for safety versus open-sourcing models for democratic access.
The convergence of AI technical vulnerabilities with global security threats.
Autonomous Weapon Systems (AWS): The ethical and tactical nightmare of deploying drones and military hardware capable of making lethal decisions without human intervention.
AI-Driven Cyber Warfare: Using AI to automate the discovery of zero-day software vulnerabilities, orchestrate mass phishing attacks, and bypass biometric security.
Defensive AI Engineering: The scramble to build AI-driven defensive shields to detect deepfakes, track automated botnets, and patch software in real-time.
Where computational intelligence directly alters physical well-being and the planet.
Dual-Use Bio-Design Risks: The terrifying reality that AI models used to discover life-saving therapeutics can easily be repurposed to design novel chemical weapons or viruses.
Automated Medical Diagnostics and Liability: The legal and ethical quagmire of who is at fault (the doctor, the hospital, or the software company) when an AI misdiagnoses a patient.
Green AI vs. Ecological Cost: Balancing the massive carbon and water footprint of AI data centers against the potential for AI to optimize global energy grids and climate modeling.
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