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AI-Viz

6th International Conference AI&Visualisation

1. 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

For submission guidelines, visit the page submission page.

For general enquiries, and submissions, contact the Conference Co-ordinator.

For Symposium-specific enquiries reach out to each symposium coordinator.