DataTalks.Club

Interpretable AI and ML - Polina Mosolova

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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
      • ...more
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