Talk details

In the field of machine learning, explainability and interpretability are becoming increasingly essential. As AI systems integrate into critical decision-making processes, understanding and trusting these models is paramount. This talk offers a comprehensive exploration of these concepts, starting with clear definitions: explainability (describing AI system workings in human terms) and interpretability (understanding the cause of decisions).
We will address challenges in achieving explainability and interpretability, particularly in complex models like deep neural networks. Methods to enhance model explainability, will be covered, providing attendees with practical tools and insights.
The talk will also highlight inherently interpretable models and a live demonstration will showcase tools and techniques that enhance model explainability and interpretability.
Attendees will gain a clear understanding of explainability vs. interpretability, and practical strategies to address them. They will be equipped with the knowledge to apply model-agnostic approaches and leverage interpretable models, fostering trust and transparency in AI systems.
Join me for this engaging session to decode the complexities of machine learning models, ensuring they remain transparent, understandable, and trustworthy. Discover how to build AI systems that perform well and gain users' trust. This talk promises to reshape your perspective on AI models and their role in our technology-driven future.