School of Computer Science
Technical Workshop Series
Exploring Explainable AI: Methods, Challenges, and Practical Applications (1st Offering)
Presenter: Nasrin Tavakoli
Date: Tuesday, October 17th, 2023
Time: 2:30 PM –3:30 PM
Location: 4th Floor (Workshop space) at 300 Ouellette Avenue (School of Computer Science Advanced Computing Hub)
LATECOMERS WILL NOT BE ADMITTED once the presentation has begun.
Abstract:
As AI systems become more prevalent daily, understanding their decision-making processes and ensuring transparency and accountability are crucial. This workshop will provide participants with a comprehensive overview of Explainable AI, its methods, challenges, and practical applications. Problem Statement: The lack of transparency in AI systems can lead to biased outcomes, ethical concerns, and limited user trust. Explainable AI offers a solution to these issues by providing insights into the decision-making processes of AI models.
The workshop will combine theoretical discussions and hands-on demonstrations to engage participants in understanding and applying Explainable AI methods. As a result, participants will gain a deeper understanding of the challenges and potential solutions in achieving explain ability for AI systems. They will also learn how to evaluate and apply different Explainable AI methods based on their specific use cases.
Workshop Outline:
- Introduction to Explainable AI
- Why explainable AI matters
- How explainable AI works
- Benefits of explainable AI
- Methods and Approaches
- Challenges and Considerations
- Principles of Explainable AI
- Conclusion
Prerequisites:
- Basic Understanding of Machine Learning and AI
- Understanding of Model Training and Evaluation
Biography:
Nasrin Tavakoli is a Ph.D. student of Computer Science at the University of Windsor. Her field of study has been Artificial Intelligence and Machine Learning. During her master's program, she worked on breast cancer diagnosis based on deep features and published two papers in international journals and conferences. She is continuing her research in Artificial Intelligence, specifically on Explainable AI, in the Ph.D. program.