Knowledge Graphs and Explainable AI as Complementary Resources for Urban Mining
📰 ArXiv cs.AI
Learn how knowledge graphs and explainable AI can improve urban mining by enhancing decision defensibility and transparency
Action Steps
- Build a knowledge graph to represent domain expertise and relationships
- Apply explainable AI techniques to provide transparent and defensible decisions
- Configure AI models to utilize knowledge graphs for improved accuracy and interpretability
- Test the effectiveness of knowledge graphs and explainable AI in urban mining scenarios
- Compare the results of AI-supported decisions with traditional methods to evaluate improvements
Who Needs to Know This
Data scientists and urban mining professionals can benefit from this knowledge to improve pre-demolition assessment processes and decision-making
Key Insight
💡 Explainable AI and knowledge graphs can complement each other to improve decision defensibility and transparency in urban mining
Share This
🔍 Enhance urban mining with knowledge graphs and explainable AI for more transparent and defensible decisions
Key Takeaways
Learn how knowledge graphs and explainable AI can improve urban mining by enhancing decision defensibility and transparency
Full Article
Title: Knowledge Graphs and Explainable AI as Complementary Resources for Urban Mining
Abstract:
arXiv:2607.09578v1 Announce Type: new Abstract: Pre-demolition assessment, the regulated audit process at the heart of urban mining, is an information process in which AI support must serve qualified auditors who remain accountable for the decisions taken. The relevant unit of value is not prediction accuracy alone, but the defensibility of the supported decisions: their legibility, plausibility, sourcing, and contestability. Explainable AI techniques and domain knowledge graphs each address par
Abstract:
arXiv:2607.09578v1 Announce Type: new Abstract: Pre-demolition assessment, the regulated audit process at the heart of urban mining, is an information process in which AI support must serve qualified auditors who remain accountable for the decisions taken. The relevant unit of value is not prediction accuracy alone, but the defensibility of the supported decisions: their legibility, plausibility, sourcing, and contestability. Explainable AI techniques and domain knowledge graphs each address par
DeepCamp AI