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54th LISBON World Conference on Artificial Intelligence: Challenges, Applications & Impacts (LAICAI-26) Oct. 8-10, 2026 Lisbon (Portugal)

LAICAI-26

URL: https://fenp.org/conference/351
Event Date: 2026-10-08 ~ 2026-10-10
Submission Date: 2026-09-17
Organizer: FENP
Location: Lisbon, Portugal

Artificial Intelligence

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.

2. 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.

3. 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.

4. 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.

5. 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).

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