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Interpretable AI and ML - Polina Mosolova
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Manage episode 370709866 series 2831626
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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
167 episoade
MP3•Pagina episodului
Manage episode 370709866 series 2831626
Content provided by DataTalks.Club. All podcast content including episodes, graphics, and podcast descriptions are uploaded and provided directly by DataTalks.Club or their podcast platform partner. If you believe someone is using your copyrighted work without your permission, you can follow the process outlined here https://ro.player.fm/legal.
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
167 episoade
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