Interpretable AI and ML - Polina Mosolova

DataTalks.Club

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