Explainable Recommendation Using Attentive Multi-View Learning
The research presented at the 33rd AAAI Conference on Artificial Intelligence focuses on developing an explainable deep model for recommendation systems. It addresses challenges in extracting explicit features from noisy data and proposes a Deep Explicit Attentive Multi-View Learning Model. This mod
0 views • 19 slides
Demystifying Explainable AI (XAI) for Better Decision-making
Explainable AI (XAI) bridges the gap between complex AI models and human understanding by providing insights into how and why decisions are made. It matters because XAI can prevent errors, biases, and adversarial attacks in AI systems. Complexity in AI models affects accuracy and explainability, wit
0 views • 19 slides
Evolution of AI and the Role of Ontologies in Explainable AI
The Evolution of AI from prehistory to the modern era, exploring key milestones in the development of Artificial Intelligence. It covers the transition from early Neural Networks to the current focus on Explainable AI, highlighting DARPA's XAI program and techniques for achieving a balance between p
0 views • 7 slides