General advice for better IML

Exploring the world of explainable AI and interpretable machine learning

Target audience

What you should know

Understanding the what and why your models predict

Variable importance and reason codes

Comparing IML and XAI

Trends in AI making the XAI problem more prominent

Local and global explanations

XAI for debugging models

KNIME support of global and local explanations

Challenges of variable attribution with linear regression

Challenges of variable attribution with neural networks

Rashomon effect

What qualifies as a black box?

Why do we have black box models?

What is the accuracy interpretability tradeoff?

The argument against XAI

Introducing KNIME

Building models in KNIME

Understanding looping in KNIME

Where to find available KNIME support for XAI

Providing global explanations with partial dependence plots

Using surrogate models for global explanations

Developing and interpreting a surrogate model with KNIME

Permutation feature importance

Global feature importance demo

Developing an intuition for Shapley values

Introducing SHAP

Using LIME to provide local explanations for neural networks

What are counterfactuals?

KNIME's Local Explanation View node

XAI View node demonstrating KNIME

Why feature engineering is critical for IML

CORELS and recent trends

Continuing to explore XAI