Interpretable AI and ML - Polina Mosolova

DataTalks.Club

English - July 07, 2023 17:00 - 52 minutes - 60.4 MB - ★★★★★ - 7 ratings
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Previous Episode: From Scratch to Success: Building an MLOps Team and ML Platform - Simon Stiebellehner

Next Episode: Why Machine Learning Design is Broken - Valerii Babushkin

We talked about:

Polina's background
How common it is for PhD students to build ML pipelines end-to-end
Simultaneous PhD and industry experience
Support from both the academic and industry sides
How common the industrial PhD setup is and how to get into one
Organizational trust theory
How price relates to trust
How trust relates to explainability
The importance of actionability
Explainability vs interpretability vs actionability
Complex glass box models
Does the explainability of a model follow explainability?
What explainable AI bring to customers and end users
Can all trust be turned into KPI?

Links:

LinkedIn: https://www.linkedin.com/in/polina-mosolova/
Neural Additive Models paper: https://proceedings.neurips.cc/paper/2021/file/251bd0442dfcc53b5a761e050f8022b8-Paper.pdf
Neural Basis Model paper: https://arxiv.org/pdf/2205.14120.pdf
Interpretable Feature Spaces paper: https://kdd.org/exploration_files/vol24issue1_1._Interpretable_Feature_Spaces_revised.pdf