Call For Papers - The World's Largest Index/List of Call For Papers

55th ISTANBUL World Congress on Artificial Intelligence: Impacts, Challenges & Applications (AIICA-26) Nov. 23-25, 2026 Istanbul (Türkiye)

AIICA-26

URL: https://fenp.org/conference/354
Event Date: 2026-11-23 ~ 2026-11-25
Submission Date: 2026-11-02
Organizer: FENP

Artificial Intelligence

Call for papers/Topics

Topics of interest for submission include any topics related to:

Part 1: Independent Pillars (The Core Taxonomy)

These are the primary topics and subtopics categorized neatly by their native domains.

1. Applications (Where AI is Used)

Healthcare & Life Sciences
- Subtopics: AI-driven drug discovery, predictive diagnostics (radiology/imaging), personalized medicine, robotic surgery, genomic data analysis.

Finance & Commerce
- Subtopics: Algorithmic trading, fraud detection, automated credit scoring, algorithmic dynamic pricing, supply chain optimization.

Creative & Generative Arts
- Subtopics: Text-to-image/video generation, automated journalism, synthetic music production, localized content translation.

Autonomous Systems & Robotics
- Subtopics: Self-driving vehicles, drone delivery networks, warehouse automation (cobots), smart grid management.

Education & Knowledge Management
- Subtopics: Adaptive learning platforms, automated grading, intelligent tutoring systems, automated taxonomy building.

2. Impacts (The Outcomes of AI)

Economic & Labor Impacts
- Subtopics: Increased macroeconomic productivity, job displacement (automation), job creation (AI trainers/prompt engineers), shift toward high-cognitive labor.

Social & Cultural Impacts
- Subtopics: Democratization of creative tools, shifts in human communication, digital divide (high-resource vs. low-resource nations), echo chambers via algorithm curation.

Environmental Impacts
- Subtopics: Climate modeling and smart resource distribution (positive), massive energy/water consumption of data centers (negative).

3. Challenges (The Obstacles & Risks)

Technical & Operational Challenges
- Subtopics: AI "hallucinations" and unreliability, data scarcity (especially for low-resource languages), compute and infrastructure costs, legacy system interoperability.

Ethical & Human Rights Challenges
- Subtopics: Algorithmic bias and discrimination, loss of human agency, deepfakes and non-consensual synthetic media, copyright and IP infringement.

Security & Governance Challenges
- Subtopics: Data privacy violations, adversarial attacks (prompt injection), complex liability (who is at fault when an AI fails?), lack of unified global regulation.

Part 2: Interrelated & Cross-Disciplinary Topics

Real-world scenarios rarely stay in one box. The most critical areas of study in AI today exist at the intersection of applications, challenges, and impacts.

1. The Intersection of Application $\rightarrow$ Challenge

When we take a specific application and try to deploy it, unique challenges arise.

Clinical AI vs. Liability & Safety: If a diagnostic AI misinterprets an MRI, who holds the liability? The hospital, the developer, or the doctor who trusted it?

Generative AI vs. Intellectual Property: Large Language Models (LLMs) and diffusion models are applied to create commercial art, triggering massive challenges regarding copyright and artist compensation.

Autonomous Vehicles vs. Edge-Case Ethics: Applying AI to self-driving cars forces developers to program solutions to ethical dilemmas (e.g., the classic "Trolley Problem").

2. The Intersection of Application / Impact

How putting a tool to work ripples out to change society.

Algorithmic Finance to Market Volatility: While high-frequency trading applications increase liquidity, their interconnected nature can trigger systemic risks like "flash crashes."

AI in Human Resources to Socioeconomic Inequality: Using automated screening applications to filter resumes often inadvertently scales historical human biases, impacting marginalized groups' access to jobs.

3. The Intersection of Challenge to Impact

How our failure (or success) in handling a challenge dictates the ultimate societal outcome.

The Black Box Problem/ Public Trust: Because deep learning models are often unexplainable (a technical challenge), it directly impacts whether the public and regulators will adopt or reject them (a social impact).

Compute Intensity / Geopolitical Power Concentration: The massive energy and financial costs required to train top-tier AI models mean that only a few tech giants in wealthy nations can build them, creating an impact of unchecked corporate power.