Using Learning Theories to Evolve Human-Centered XAI: Future Perspectives and Challenges
📰 ArXiv cs.AI
Learn how to apply learning theories to evolve human-centered Explainable AI (XAI) for better transparency and understanding
Action Steps
- Apply learning theories to XAI development to improve model transparency
- Use cognitive load management to optimize explanation complexity
- Integrate feedback mechanisms to adapt explanations to user needs
- Evaluate XAI systems using human-centered metrics
- Develop XAI frameworks that incorporate learning theories and user feedback
Who Needs to Know This
AI researchers and developers can benefit from this knowledge to create more explainable and transparent AI systems, while data scientists and analysts can use it to improve their understanding of complex AI models
Key Insight
💡 Learning theories can be used to improve XAI by making explanations more effective and user-centered
Share This
🤖💡 Apply learning theories to evolve human-centered XAI and improve AI transparency! #XAI #AItransparency
Key Takeaways
Learn how to apply learning theories to evolve human-centered Explainable AI (XAI) for better transparency and understanding
Full Article
Title: Using Learning Theories to Evolve Human-Centered XAI: Future Perspectives and Challenges
Abstract:
arXiv:2604.19788v1 Announce Type: new Abstract: As Artificial Intelligence (AI) systems continue to grow in size and complexity, so does the difficulty of the quest for AI transparency. In a world of large models and complex AI systems, why do we explain AI and what should we explain? While explanations serve multiple functions, in the face of complexity humans have used and continue to use explanations to foster learning. In this position paper, we discuss how learning theories can be infused i
Abstract:
arXiv:2604.19788v1 Announce Type: new Abstract: As Artificial Intelligence (AI) systems continue to grow in size and complexity, so does the difficulty of the quest for AI transparency. In a world of large models and complex AI systems, why do we explain AI and what should we explain? While explanations serve multiple functions, in the face of complexity humans have used and continue to use explanations to foster learning. In this position paper, we discuss how learning theories can be infused i
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