37th ISTANBUL International Conference on Artificial Intelligence: Applications, Challenges & Impacts: AIACI-26

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