Introduce the black box nature of AI models, the importance of interpretability and transparency of AI models (e.g., safety, security, and bias in models and datasets) and methods proposed to explain or reveal the ways that AI models make decisions.
Breakdown
The Black Box Dilemma
Why deep learning models are often perceived as "black boxes"
The importance of transparency and interpretability in scientific applications
Principles of Scientific Soundness
Reproducibility: Ensuring experiments can be replicated by others
Robustness: Model performance across different datasets and conditions
Generalizability: How well models perform on unseen data
Introduction to Model Explainability
What is model explainability and why is it crucial?
Differences between global and local explainability
Techniques for Model Interpretation
Feature Visualization: Understanding what features a model has learned
Saliency Maps: Highlighting important regions in input data
Activation Maximization: Visualizing what maximally activates certain neurons
SHAP (SHapley Additive exPlanations): Game theoretic approach to explain output of any machine learning model
Ensuring Scientific Soundness in Deep Learning
Data integrity: Ensuring data quality and addressing biases
Model validation: Techniques beyond traditional train-test splits (e.g., k-fold cross-validation)
Uncertainty quantification: Understanding and communicating model uncertainty
Case Studies: Failures and Successes
Real-world examples where lack of explainability or scientific rigor led to issues
Success stories where proper model interpretation and validation made a difference
Q&A and Discussion
Encouraging sharing of personal experiences or challenges related to model explainability and scientific soundness
Discussing potential future developments in the field of model interpretability
AI tools
Zetane viewer (an open-source AI model explanation tool)
Model explainability and scientific soundness
Goal
Introduce the black box nature of AI models, the importance of interpretability and transparency of AI models (e.g., safety, security, and bias in models and datasets) and methods proposed to explain or reveal the ways that AI models make decisions.
Breakdown
AI tools
Zetane viewer (an open-source AI model explanation tool)