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
