Alter Everything
162: The Power of Explainable AI
Jul 3, 2024
Industry expert and Director of Data Science at Western Digital, Srinimisha Morkonda Gnanasekaran, discusses the importance of explainable AI in AI decision-making. Topics include strategies for implementation, real-world examples of misclassifications, and techniques like decision trees and SHAP for model interpretability. The podcast also explores key considerations for successful AI adoption and essential skills for success in data science and AI.
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Quick takeaways
- Explainable AI is crucial for trust and transparency in AI systems, especially in high-stakes domains like healthcare and finance.
- Intrinsic and post hoc techniques can improve AI explainability by simplifying models, providing insights into feature importance, and aiding in understanding individual feature impacts.
Deep dives
The Significance of Explainable AI
Explainable AI has been a topic of interest since the evolution of the field, with a milestone around 2015-2016 highlighting the importance of understanding model decisions. An example presented a scenario where a model misclassified a husky as a wolf due to considering the background snow. The essence of explainable AI lies in mimicking human decision-making, providing step-by-step insights into model predictions.
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