AIACI-26 2026 - 37th ISTANBUL International Conference on Artificial Intelligence: Applications, Challenges & Impacts (AIACI-26) scheduled on April 2-4, 2026 Istanbul (Türkiye)

Website https://ureng.urst.org/conference/165 | Edit Freely

Category Artificial Intelligence: Applications, Challenges & Impacts

Deadline: March 02, 2026 | Date: April 02, 2026-April 04, 2026

Venue/Country: Istanbul, Turkey

Updated: 2025-12-09 18:51:18 (GMT+9)

Call For Papers - CFP

I. Applications of Artificial Intelligence

This section covers the practical uses of AI across different industries and the core technical domains driving these applications.

A. Core AI Domains & Techniques

Machine Learning (ML):

Supervised Learning (Classification, Regression)

Unsupervised Learning (Clustering, Dimensionality Reduction)

Reinforcement Learning (RL, Deep Q-Networks)

Deep Learning (Artificial Neural Networks, CNNs, RNNs/LSTMs)

Generative AI:

Large Language Models (LLMs - e.g., GPT, BERT)

Text-to-Image/Video Models (GANs, Diffusion Models)

Code Generation and Programming Assistance

Natural Language Processing (NLP):

Machine Translation and Sentiment Analysis

Chatbots and Virtual Assistants

Speech Recognition and Text-to-Speech (TTS)

Computer Vision (CV):

Image Recognition and Object Detection

Facial Recognition and Biometrics

Autonomous Navigation (Self-Driving Cars, Drones)

Robotics and Automation:

Industrial Automation (Manufacturing, Assembly)

Robotic Process Automation (RPA)

Autonomous Systems (Drones, Mobile Robots)

B. Industry-Specific Applications

Healthcare and Medicine:

Diagnostic Imaging and Disease Detection (Radiology, Pathology)

Drug Discovery and Pharmaceutical Research

Personalized Treatment Plans and Genomics

Patient Monitoring and Health Wearables

Finance and Fintech:

Fraud Detection and Cybersecurity

Algorithmic Trading and Quantitative Analysis

Credit Scoring and Risk Assessment

Customer Service Chatbots

E-commerce and Retail:

Recommendation Systems and Personalized Marketing

Inventory Management and Supply Chain Optimization

Dynamic Pricing Models

Visual Search

Manufacturing and Logistics:

Predictive Maintenance of machinery

Quality Control and Defect Detection

Warehouse Automation and Route Optimization

Science and Research:

Climate Modeling and Environmental Monitoring

Astronomy (Data Analysis of Telescopic Imagery)

Materials Science (Simulating new material properties)

II. Challenges of Artificial Intelligence

This section explores the technical, ethical, and implementation hurdles that impede the responsible development and deployment of AI systems.

A. Ethical and Societal Challenges

Algorithmic Bias and Fairness:

Bias in Training Data (Racial, Gender, Socio-economic)

Discriminatory Outcomes (Hiring, Loan Approvals, Criminal Justice)

Mitigation Strategies and Auditing

Transparency and Explainability (XAI):

The "Black Box" Problem in Deep Learning

Need for Trust and Auditability in Critical Systems

Methods for Model Interpretation

Privacy and Data Security:

Data Hunger of AI Models and Data Governance

Vulnerability to Data Poisoning and Model Inversion Attacks

Differential Privacy and Federated Learning

Misinformation and Malicious Use:

Deepfakes and Synthetic Media Generation

Weaponization of AI (Autonomous Weapons Systems - AWS)

Cybersecurity Threats (AI-powered attacks and defense)

B. Technical and Implementation Challenges

Data Quality and Availability:

Need for Massive, High-Quality, and Labeled Datasets

Data Scarcity for Rare Events or Low-Resource Languages

Resource Intensity:

High Computational Costs (Training LLMs/Foundation Models)

Energy Consumption and Environmental Impact

Reliability and Robustness:

Model Drift and Out-of-Distribution Data Handling

Adversarial Attacks and System Failures

Integration and Adoption:

Lack of AI Talent and Expertise in Organizations

High Initial Investment Costs (Hardware, Software)

Data Silos and Interoperability Issues

III. Impacts of Artificial Intelligence

This section focuses on the transformative effects of AI on the economy, workforce, and global governance.

A. Economic and Workforce Impacts

Job Displacement and Transformation:

Automation of Routine Tasks (Blue-collar and White-collar)

Creation of New Jobs and Skill Demands (Prompt Engineers, AI Trainers)

The Need for Reskilling and Upskilling Initiatives

Productivity and Growth:

Increased Business Efficiency and Process Optimization

Accelerated Scientific Discovery and Innovation

Impact on Global Competitiveness and Economic Disparity

Wealth and Power Concentration:

Dominance of Large Technology Companies (Big Tech)

Socio-Economic Inequality (The "Haves" and "Have-Nots" of AI access)

B. Legal, Regulatory, and Governance Impacts

Regulation and Policy:

The Need for Global and Regional AI Frameworks (e.g., EU AI Act)

Standardization for AI Safety and Testing

Intellectual Property and Copyright:

Ownership of AI-Generated Content (Art, Code)

Data Licensing and Use of Copyrighted Data in Training

Liability and Accountability:

Determining Legal Responsibility in AI Failures (Autonomous Vehicles, Medical Diagnosis)

Oversight Mechanisms and Auditing Requirements

Future of Humanity and Existential Risk:

The path toward Artificial General Intelligence (AGI)

Long-term Safety and Alignment of Superintelligent Systems

Philosophical Questions of Consciousness and Sentience