37th ISTANBUL International Conference on Artificial Intelligence: Applications, Challenges & Impacts (AIACI-26) scheduled on April 2-4, 2026 Istanbul (Türkiye)
AIACI-26
- URL: https://ureng.urst.org/conference/165
- Event Date: 2026-04-02 ~ 2026-04-04
- Submission Date: 2026-03-19
- Organizer: URENG
- Location: Istanbul, Turkey
Call for Papers: AIACI-26
Full Articles/ Reviews/ Shorts Papers/ Abstracts are welcomed in the following research fields.
Artificial Intelligence: Applications, Challenges & Impacts
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
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