Explainable AI (XAI) Program
Making AI Transparent, Trustworthy, and Accountable
Skills you will gain:
This self-paced program covers state-of-the-art techniques and methodologies for interpreting complex AI models. Participants will learn to apply these techniques to make AI systems more transparent, enhancing trust and accountability in AI applications.
Aim: To provide PhD scholars and academicians with advanced skills in making AI models transparent and understandable, ensuring their applications are interpretable and trustworthy in critical fields like healthcare and finance.
Program Objectives:
- Understand the need for explainable AI.
- Learn various techniques for making AI models interpretable.
- Apply XAI methods in real-world scenarios.
- Analyze the impact of XAI on different industries.
- Ensure compliance with ethical and regulatory standards.
What you will learn?
- Introduction to Explainable AI
- The Importance of XAI in Various Industries
- Methods for Model Interpretability
- Feature Importance and Attribution
- Model-Agnostic Techniques
- Example-Based Explanations
- XAI in Practice: Tools and Frameworks
- Case Studies: XAI in Healthcare
- Case Studies: XAI in Finance
- Ethical and Regulatory Considerations
- Future Trends in Explainable AI
Intended For :
Domain: AI researchers, data scientists, machine learning engineers, healthcare analysts, finance professionals, and academic researchers.
Career Supporting Skills
Interpretability Transparency Attribution Model-Agnostic Compliance Ethical Analysis
