. Explainable and Interpretable AI through Visualisation

Techniques for making AI models understandable to non-experts
Visualising model decisions for accountability and transparency
Overcoming challenges in visualising complex neural networks
Human-in-the-loop approaches to enhance AI interpretability
2. AI & Visual Knowledge Discovery

Visualizations of ML model results and properties
Visual interactive AI/ML model discovery
Lossless visualization of AI/ML high-dimensional data
Interactive ML algorithms for high-stakes AI/ML tasks with human-in-the-loop
Methods to counter quasi-explanations of AI/ML models
Investigation of the trade-offs between model complexity and interpretability
visualization techniques for explaining the decision-making processes of ML models
visualization of feature selection and extraction techniques
Transparent and interpretable visualization of ensemble methods
Visualization of the model’s uncertainty and risk assessment
3. Visual Analytics

Data Visualisation for Big Data Analytics and AI
Scalable visualisation techniques for high-dimensional data
Real-time visualisation of streaming data in AI applications
Visual analytics for big data and large-scale AI models
AI-enhanced visualisations for identifying trends in large datasets
4. Multimodal AI and Cross-Modal Visualisations _ Green metaverse

Combining text, image, and video in visual AI applications
Interactive visualisation of multimodal data sources
Challenges and opportunities in fusing modalities for analysis
Applications of multimodal visualisations in real-world scenarios
5. Virtual Reality (VR), Augmented Reality (AR), and Immersive AI

Integrating AI with VR and AR for immersive visual experiences
Visualisation of AI-generated content in immersive environments
Applications in education, training, and simulation
User experience design and interaction in AI-driven AR/VR
6. AI-Powered Visualisation in Smart Cities and Urban Analytics

Visualizing IoT and sensor data for urban decision-making
AI and visualisation for traffic, pollution, and resource management
Augmented reality and interactive displays for urban data
Applications of AI and visualisation in public safety and infrastructure
7. Edge Computing and Real-Time Visualisation with AI

Challenges of AI and visualisation on edge devices
Real-time visual analytics for autonomous vehicles and robotics
Efficient visualisation in low-latency applications
AI-driven visualisations in IoT networks and smart devices
8. Visualizing Uncertainty and Risk in AI Predictions

Methods to visualise uncertainty in AI model outputs
Applications in finance, healthcare, and risk assessment
Improving decision-making with uncertainty visualisations
User perceptions of risk and uncertainty in AI-driven insights
9. Visual Storytelling with AI-Generated Content

AI-enhanced storytelling for data narratives and communication
Tools for automating visual storytelling in journalism
User-centered design in AI-assisted storytelling interfaces
Case studies on AI in interactive media and entertainment
10. Human-AI Collaboration in Visualisation and Decision Support

Designing visualisation tools for collaborative AI analysis
Enhancing user trust through interactive AI visualisations
Augmenting human intuition with AI-assisted visualisation
Case studies in healthcare, finance, and industry
11. AI-Driven Personalized Visualisation Experiences

Adaptive visualisation techniques for personalised insights
Using AI for recommendation and customisation in dashboards
User profiling and personalisation in data visualisation
Implications of personalisation on user engagement and understanding
12. Future Directions in Quantum AI and Visualisation

Opportunities and challenges of quantum-enhanced AI visualisation
Quantum computing for complex data visualisation tasks
Potential applications in scientific research and simulations
Current limitations and anticipated breakthroughs
13. Prompt Engineering with Visualisation – Visual Prompt

Introduction to Prompt Engineering
Visualisation-Aided Prompt Design
Evaluating AI Responses
Use Cases
AI-driven visualisation workflows (e.g., data dashboards, network diagrams);
Industry applications in education, business, and data science.
Advanced Techniques
Leveraging iterative prompts for dynamic visual models.
Combining visual and textual inputs for richer outputs.
14. Ethical AI and Visualisation: Transparency, Fairness, and Trust

Using visualisation to detect and mitigate bias in AI models
Visual tools for AI ethics and responsible AI practices
Privacy-preserving visualisation techniques
Visual approaches for auditing AI systems