#70 Making Black Box Models Explainable with Christoph Molnar – Interpretable Machine Learning Researcher

Christoph Molnar is a data scientist and Ph.D. candidate in interpretable machine learning. He is interested in making the decisions from algorithms more understandable for humans. Christoph is passionate about using statistics and machine learning on data to make humans and machines smarter.

Enjoy the show!

We speak about:

[02:10] How Christoph started in the data space

[09:25] Understanding what a researcher needs

[15:15] Skills learned from software engineers

[16:00] Statistical consulting

[19:50] Labeling data

[23:00] Christoph is pursuing his Ph.D.

[29:00] Why is interpretable machine learning needed now?

[31:00] Learning interpretability

[33:50] Accumulated local effects (ALE)

[37:00] Example-based explanations

[39:15] Deep learning

[43:35] The illustrations in Interpretable Machine Learning.

[49:50] How Christoph maximizes the impact of his time

Resources:

Christoph’s LinkedIn: https://www.linkedin.com/in/christoph-molnar-63777189/

Christoph’s Website: https://christophm.github.io

Interpretable Machine Learning: https://christophm.github.io/interpretable-ml-book/

Quotes:

“Always look at the process when labeling data.”

“After each chapter of my book, I publish it and get feedback.”

“I randomly read a lot of papers and structure the knowledge to fit them together.”

“I express what I want easier with illustrations in my book.”

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