54th LISBON World Conference on Artificial Intelligence: Challenges, Applications & Impacts (LAICAI-26)

Call for Papers/Topics

Topics of interest for submission include any topics related to:

1. Core Foundations

Before diving into impacts, these topics define the capabilities of the system.

• Machine Learning (ML): Supervised, unsupervised, and reinforcement learning.
• Deep Learning: Neural networks, CNNs (vision), and RNNs (sequences).
• Generative AI: Large Language Models (LLMs), diffusion models, and synthetic media.
• Natural Language Processing (NLP): Sentiment analysis, translation, and semantic understanding.
• Computer Vision: Image recognition, spatial awareness, and video analysis.

1. Key Applications

AI is no longer theoretical; it is embedded in global infrastructure.

• Healthcare:
  • AI-driven diagnostics and medical imaging.
  • Drug discovery and genomic sequencing.
  • Personalized treatment plans.
• Finance:
  • Algorithmic trading and risk assessment.
  • Fraud detection and automated credit scoring.
• Transportation & Logistics:
  • Autonomous vehicles and drone delivery.
  • Supply chain optimization and predictive maintenance.
• Creative Industries:
  • AI-generated art, music, and literature.
  • Automated video editing and game design.

1. Major Challenges

These are the technical and structural hurdles preventing "perfect" AI integration.

• Technical Limitations:
  • Hallucinations: LLMs generating confident but false information.
  • Data Scarcity/Quality: The "garbage in, garbage out" problem.
  • Explainability (Black Box Problem): The difficulty in understanding how an AI reached a specific decision.
• Security Vulnerabilities:
  • Adversarial Attacks: Inputting data designed to trick AI models.
  • Model Inversion: Privacy leaks where training data can be extracted.

1. Ethical & Philosophical Impacts

This is where AI intersects with human values and social structures.

• Bias and Fairness:
  • Algorithmic bias (racial, gender, and socioeconomic prejudices in data).
  • The digital divide: Who gets access to AI first?
• Labor and Economy:
  • Job displacement vs. job augmentation.
  • The transition to an "AI-first" workforce and reskilling needs.
• Governance and Law:
  • Copyright and IP ownership of AI-generated content.
  • Regulation (e.g., EU AI Act) and international AI safety standards.
• Existential Risks & Safety:
  • Alignment Problem: Ensuring AI goals match human values.
  • Superintelligence and long-term safety concerns.

1. Interrelated Themes

These topics bridge multiple categories simultaneously.

• Environmental Impact: The massive energy consumption of training models (Application vs. Sustainability).
• Human-AI Interaction: How reliance on AI affects human cognition and social skills (Impact vs. Design).
• Data Privacy: The tension between needing massive datasets for accuracy and protecting individual rights (Challenge vs. Ethics).