CFP - ICAIH AI Horizons
January 19-25, 2026
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ETLTC-ICAIH2026 is to be co-hosted by the University of Aizu, Japan & Jindal Global University, India
Prof. Adya Surbhi
Prof. Adya Surbhi
Associate Professor, Jindal Global Law School, Jindal Global University
Chair - ICAIH AI Horizons
Prof. Harsh Mahaseth
Prof. Harsh Mahaseth
Associate Professor, Jindal Global Law School, Jindal Global University
Co-Chair - ICAIH AI Horizons
INVITED CONFERENCE TRACK:
🎯 Conference Track Title:
🎯 Conference Track Title:
Artificial Intelligence and International Affairs: Power, Policy, and Global Governance
🧭 Track Overview:
🧭 Track Overview:
This track explores how AI technologies are reshaping the landscape of global politics, diplomacy, security, and international cooperation. As AI becomes a strategic asset for nations, it raises urgent questions about geopolitical competition, cyber diplomacy, ethics in warfare, digital sovereignty, and transnational regulation. This track invites interdisciplinary scholars and practitioners to examine the political, legal, and institutional implications of AI on the global stage.
🔍 Suggested Themes / Topics:
🔍 Suggested Themes / Topics:
AI and Global Power Competition
AI and Geopolitics of Data
Cybersecurity, AI, and Conflict
AI Diplomacy and Multilateral Governance
AI and International Development
AI Ethics as a Foreign Policy Tool
Transnational Collaboration on AI R&D
Regulatory Divergence and Convergence
AI and Human Rights in Global Contexts
CFP - TOPICS INCLUDE BUT NOT LIMITED TO:
CFP - TOPICS INCLUDE BUT NOT LIMITED TO:
ARTIFICIAL INTELLIGENCE IN LAW:
Legal Research and Document Analysis:
AI-powered legal research tools and platforms
Natural language processing (NLP) for legal document analysis
Automated case law and precedent retrieval systems
Contract Analysis and Management:
Machine learning for contract review and analysis
Intelligent contract management systems
Contract automation and standardization using AI
Predictive Analytics and Decision Support:
AI-driven prediction models for legal outcomes
Data-driven decision-making in legal practice
Risk assessment and legal strategy optimization using AI
E-Discovery and Litigation Support:
Technology-assisted review (TAR) in e-discovery
AI applications for early case assessment
Automated legal document review and privilege detection
Regulatory Compliance and Risk Management:
AI-based compliance monitoring and reporting
Risk prediction and mitigation using machine learning
Ethical and legal implications of AI in regulatory compliance
Legal Chatbots and Virtual Assistants:
Conversational AI for legal advice and assistance
Virtual legal assistants and automated client support
Challenges and Limitations of AI Chatbots in legal practice
Privacy, Security, and Data Governance:
AI Techniques for data protection and privacy compliance
Security Implications of AI in legal workflows
Legal and ethical considerations of AI in data governance
Ethics, Bias, and Explainability in AI:
Addressing bias and fairness issues in legal AI systems
Explainable AI for legal decision making
Ethical Guidelines and Regulations for AI in the legal field
Ethical and Legal Challenges of AI in the Legal Profession:
Ethical Considerations in the Use of AI in legal practice
Legal Liability and Responsibility in AI-generated Outcomes
Impact of AI on the Role of legal professionals and the Future of legal education
AI in Legal Compliance and Regulatory Systems:
AI applications for regulatory compliance monitoring and reporting
Automated compliance risk assessment and management
Challenges and Opportunities of AI in Regulatory and compliance frameworks
EMERGING ARTIFICIAL INTELLIGENCE TECHNOLOGIES & TRENDS
AI in Edge Computing and Internet of Things (IoT):
AI algorithms and models optimized for edge devices and resource-constrained environments
Edge AI architectures and frameworks for real-time data processing and decision making
AI-enabled IoT applications and use cases in various industries
Security and privacy considerations in AI-powered edge computing
Challenges and Opportunities of AI at the Edge in a connected world
Generative AI: From Text to Images and Beyond:
Advances in generative models such as GANs (Generative Adversarial Networks) and VAEs (Variational Autoencoders)
Text-to-image synthesis and generation of realistic visual content
Creative applications of generative AI in art, design, and content creation
Ethical Considerations and potential misuse of Generative AI Technologies
Future Directions and Challenges in Generative AI Research and Applications
Explainable AI and Interpretable Machine Learning:
Methods and techniques for explaining AI model predictions and decisions
Interpretable machine learning models and frameworks
Transparency and accountability in AI systems
Addressing Biases and Fairness in explainable AI
User-centric Approaches to Explainability and Interpretability in AI
AI in Healthcare and Medical Applications:
AI-enabled medical diagnosis and decision support systems
Predictive analytics and early detection using machine learning in healthcare
Personalized medicine and treatment planning with AI
Ethical and legal considerations in AI Adoption in Healthcare
AI-driven healthcare innovations and future trends
AI for Social Good and Sustainable Development:
AI applications for addressing societal challenges such as poverty, education, and healthcare
AI-driven solutions for environmental sustainability and climate change mitigation
Ethical Considerations in Using AI for social good
Collaborative Approaches and Partnerships for Leveraging AI in sustainable development
Case Studies and success stories of AI initiatives making a positive social impact
Quantum Computing and AI:
Intersection of quantum computing and AI research
Quantum machine learning algorithms and models
Quantum-inspired optimization algorithms for AI applications
Quantum neural networks and quantum deep learning
Practical Applications and future prospects of quantum computing in AI
AI IN INTELLECTUAL PROPERTY (IP) PROTECTION AND TRADEMARK RECOGNITION
AI Techniques for Intellectual Property Protection:
AI-based approaches for detecting and preventing copyright infringement
Machine learning algorithms for identifying and protecting trade secrets
Automated monitoring and enforcement of intellectual property rights
AI-assisted identification and removal of counterfeit products
Legal and ethical considerations in Using AI for IP Protection
Advances in Trademark Recognition with AI:
Deep learning models for trademark recognition and classification
Computer vision techniques for automated trademark image analysis
AI-driven trademark search and monitoring systems
Trademark similarity assessment using machine learning algorithms
Challenges and future directions in AI-based trademark recognition
AI-assisted Patent Analysis and Prior Art Search:
Natural language processing (NLP) techniques for patent analysis and understanding
Machine learning models for automated prior art search and patent invalidation
AI-driven tools for patent landscape analysis and technology mapping
Semantic Analysis and Knowledge Graph Applications in Patent Examination
Ethical and legal implications of AI in patent analysis and prior art search
AI-powered Brand Monitoring and Reputation Management:
AI-driven systems for monitoring online brand presence and reputation
Sentiment analysis and social media monitoring for brand perception analysis
Proactive brand protection using machine learning and data analytics
AI-enabled crisis management and response in brand reputation incidents
Privacy and data protection considerations in AI-powered brand monitoring
SEMANTIC WEB & KNOWLEDGE REPRESENTATION
Advances in Semantic Web Technologies and Standards:
Semantic web languages and standards (e.g., RDF, OWL, SPARQL)
Knowledge graph construction and management
Ontology design and development for the semantic web
Linked data principles and practices
Semantic web reasoning and inferencing techniques
Knowledge Representation and Reasoning in AI:
Formalisms for knowledge representation (e.g., logic, frames, ontologies)
Semantic-based reasoning and inference engines
Knowledge graph embeddings and representation learning
Explainable and interpretable AI using knowledge-based systems
Applications of knowledge representation in AI domains (e.g., healthcare, robotics, natural language processing)
Ontology Learning and Extraction:
Automatic ontology learning and extraction from unstructured and semi-structured data
Knowledge extraction techniques from text, web pages, and other sources
Ontology alignment and merging for integrating heterogeneous knowledge sources
Evaluation and benchmarking of ontology learning and extraction methods
Applications of ontology learning in domains such as healthcare, e-commerce, and information retrieval
Semantic Web and Machine Learning Integration:
Combining semantic web technologies with machine learning approaches
Semantic-enhanced machine learning models for data integration and analysis
Knowledge transfer and representation learning in the semantic web context
Incorporating semantic information into deep learning architectures
Use cases and applications of combining semantic web and machine learning in various domains
AI IN COMPUTER SCIENCE:
Computer Vision and Image Recognition:
Object detection and classification
Image segmentation
Facial recognition
Scene understanding
Visual search and image retrieval
Machine Learning for Healthcare:
Medical image analysis
Disease diagnosis and prediction
Drug discovery and development
Personalized treatment recommendations
Health monitoring and wearable devices
Robotics and Autonomous Systems:
Autonomous vehicles
Drones and UAVs (Unmanned Aerial Vehicles)
Industrial automation
Collaborative and service robots
Robotic perception and navigation
AI in Gaming and Entertainment:
Game playing agents and AI opponents
Procedural content generation
Character animation and behavior modeling
Virtual reality (VR) and augmented reality (AR) applications
AI-generated music and art
AI for Data Analytics and Decision Making:
Predictive analytics
Recommender systems
Fraud detection and anomaly detection
Optimization and resource allocation
Data-driven decision support systems
AI in Finance and Business:
Algorithmic trading
Credit risk assessment
Customer service chatbots
Supply chain optimization
Market trend analysis
Ethical and Social Implications of AI:
Bias and fairness in AI systems
Privacy and data protection
AI and job automation
AI and healthcare ethics
AI and decision-making transparency
AI in Education:
Intelligent tutoring systems
Adaptive learning platforms
Educational data mining
Automated grading and feedback
Learning analytics
AI for Environmental Sustainability:
Climate modeling and prediction
Renewable energy optimization
Environmental monitoring and assessment
Wildlife conservation and protection
Sustainable agriculture and resource management
ARTIFICIAL INTELLIGENCE IN BUSINESS & COMMERCE:
AI-Driven Business Strategy and Innovation:
AI as a driver of business transformation and disruption
AI-enabled innovation and new business models
Strategic Considerations for Adopting AI in different industries
Machine Learning and Predictive Analytics:
Applications of machine learning in Business decision making
Predictive analytics for customer behavior and market trends
AI-powered demand forecasting and inventory optimization
Intelligent Automation and Robotic Process Automation (RPA):
Streamlining business processes with AI and RPA
Cognitive automation for repetitive and rule-based tasks
Impact of intelligent automation on Workforce and job roles
Natural Language Processing (NLP) and Conversational AI:
NLP applications in customer service and chatbots
Voice assistants and virtual agents for customer interactions
Sentiment analysis and social media monitoring with NLP
Personalization and Recommendation Systems:
AI-driven personalized marketing and customer experiences
Recommendation algorithms for product and content suggestions
Dynamic pricing and personalized offers using AI
Fraud Detection and Risk Management:
AI-based fraud detection and prevention systems
Risk assessment and anomaly detection using machine learning
AI applications in cybersecurity and threat intelligence
Supply Chain Optimization and Logistics:
AI-driven supply chain planning and optimization
Intelligent logistics and transportation management
Demand forecasting and inventory optimization with AI
Ethics, Transparency, and Bias in AI:
Addressing ethical considerations in AI-driven business decisions
Ensuring fairness and transparency in AI algorithms
Mitigating Bias and Discrimination in AI Applications
AI in Finance and Investment:
Algorithmic trading and portfolio optimization using AI
AI-based credit scoring and risk assessment in lending
Financial fraud detection and anti-money laundering with AI
AI and Data Governance:
Privacy and security implications of AI in Business and Commerce
Data governance frameworks for Responsible AI Usage
Legal and regulatory challenges in AI-driven Businesses
ARTIFICIAL INTELLIGENCE IN SEMANTICS AND NATURAL LANGUAGE PROCESSING:
Advances in Semantic Understanding and Knowledge Representation:
Deep learning models for semantic understanding and representation
Knowledge graphs and ontologies for capturing and organizing semantic information
Cross-lingual and multilingual semantics in NLP applications
Semantic parsing and interpretation of natural language queries
Semantics in dialogue systems and conversational agents
Sentiment Analysis, Opinion Mining, and Emotion Detection:
Sentiment analysis techniques for understanding attitudes and opinions in text
Opinion mining for analyzing product reviews, social media posts, and customer feedback
Emotion detection and sentiment-driven applications in NLP
Fine-grained sentiment analysis and aspect-based sentiment analysis
Cross-domain and cross-lingual sentiment analysis
Discourse Analysis and Text Coherence:
Discourse parsing and analysis for understanding text structure and coherence
Coreference resolution and entity linking in discourse understanding
Text summarization and generation with a focus on coherence and cohesion
Implicit and explicit discourse relations in natural language understanding
Applications of discourse analysis in question answering, information extraction, and dialogue systems
Cross-modal and Multimodal Understanding:
Cross-modal representation learning for integrating textual and visual information
Multimodal sentiment analysis and emotion recognition using text, images, and audio
Cross-modal retrieval and alignment in multimedia data
Multimodal dialogue systems and conversational agents
Applications of multimodal understanding in areas such as healthcare, autonomous vehicles, and entertainment
AI FOR LEGAL COMPLIANCE & RISK ASSESSMENT
AI-driven Risk Assessment and Compliance Monitoring:
Machine Learning and AI Techniques for risk assessment in legal compliance
Automated monitoring and analysis of regulatory changes and requirements
Predictive analytics for identifying compliance risks and violations
AI-powered tools for continuous monitoring of compliance activities
Case studies and best practices in implementing AI for risk assessment and compliance monitoring
Explainable AI in Legal Compliance and Risk Assessment:
Explainable AI models for compliance decision-making and risk assessment
Interpretable machine learning techniques for understanding compliance predictions
Transparency and interpretability in AI-driven risk assessment systems
Legal and ethical considerations in Using AI for Compliance and risk assessment
Human-AI collaboration in compliance decision making
Natural Language Processing (NLP) for Compliance and Risk Analysis:
NLP techniques for extracting and understanding legal and regulatory text
Automated compliance document analysis and risk identification using NLP
Information extraction and entity recognition for compliance data
Sentiment analysis and opinion mining in compliance and risk assessment
Challenges and opportunities of applying NLP to legal compliance and risk analysis
AI and Machine Learning in Anti-Money Laundering (AML) and Fraud Detection:
AI models and algorithms for detecting money laundering and financial fraud
Unsupervised learning techniques for anomaly detection in financial transactions
Network analysis and pattern recognition in AML and fraud detection
Explainable AI approaches in AML and fraud detection systems
Regulatory compliance and legal considerations in AI-powered AML solutions
BUSINESS STRATEGY & AI ADOPTION
AI-Driven Business Transformation:
Developing an AI-driven business strategy for competitive advantage
Leveraging AI to drive innovation and digital transformation
Business model innovation enabled by AI technologies
Overcoming organizational challenges in implementing AI-driven transformation
Case Studies and success stories of AI-driven business transformation
Ethical and Responsible AI Adoption:
Ethical Considerations in AI Adoption and Deployment in Organizations
Responsible AI governance frameworks and practices
Mitigating bias and ensuring fairness in AI algorithms and decision-making processes
Addressing privacy and data protection concerns in AI adoption
Building Trust and Transparency in AI-powered Systems
AI-Enabled Customer Experience and Personalization:
Enhancing customer experience through AI-powered personalization
AI-driven customer segmentation and targeted marketing strategies
Chatbots and virtual assistants for customer service and support
Leveraging AI for customer sentiment analysis and feedback analysis
Ethical Considerations in the Use of AI for customer experience enhancement
AI and Data-Driven Decision-Making:
AI-driven predictive analytics for informed decision making
Extracting insights and patterns from big data using AI techniques
AI-powered recommendation systems for personalized decision support
Integrating AI algorithms into decision-making processes
Challenges and risks of relying on AI for decision making
AI FOR LEGAL & BUSINESS ANALYTICS
AI-Powered Predictive Analytics in Law and Business:
Machine learning models for predicting legal outcomes and business trends
AI-driven risk assessment and decision support in legal and business contexts
Forecasting market dynamics and customer behavior using AI algorithms
Case studies and applications of AI-powered predictive analytics in law and business
Ethical considerations and challenges in deploying AI for predictive analytics
Text Analytics and Natural Language Processing (NLP) in Legal and Business Intelligence:
NLP techniques for analyzing legal documents, contracts, and financial reports
Text mining and information extraction for legal research and due diligence
Sentiment analysis and opinion mining in business and customer feedback
Automated summarization and categorization of legal and business texts
Advancements in NLP applications for legal and business intelligence
AI in Contract Analysis and Management:
Automated contract analysis using AI and natural language processing
Contract extraction, classification, and clause identification with machine learning
AI-driven contract management systems and workflows
Contract risk assessment and mitigation using AI algorithms
Challenges and opportunities of AI adoption in contract analysis and management
AI for Fraud Detection and Compliance in Business:
AI models for detecting financial fraud and identifying fraudulent patterns
Automated compliance monitoring and risk assessment using AI techniques
Fraud prevention and detection in digital transactions and e-commerce using AI
AI-driven tools for anti-money laundering (AML) and Know Your Customer (KYC) processes
Legal and ethical implications of AI in fraud detection and compliance
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