DataTalks.Club

Responsible and Explainable AI - Supreet Kaur

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We talked about:

  • Supreet’s background
  • Responsible AI
  • Example of explainable AI
  • Responsible AI vs explainable AI
  • Explainable AI tools and frameworks (glass box approach)
  • Checking for bias in data and handling personal data
  • Understanding whether your company needs certain type of data
  • Data quality checks and automation
  • Responsibility vs profitability
  • The human touch in AI
  • The trade-off between model complexity and explainability
  • Is completely automated AI out of the question?
  • Detecting model drift and overfitting
  • How Supreet became interested in explainable AI
  • Trustworthy AI
  • Reliability vs fairness
  • Bias indicators
  • The future of explainable AI
  • About DataBuzz
  • The diversity of data science roles
  • Ethics in data science
  • Conclusion


    • Links:

    •  LinkedIn: https://www.linkedin.com/in/supreet-kaur1995/
    • Databuzz page: https://www.linkedin.com/company/databuzz-club/
    • Medium Blog Page: https://medium.com/@supreetkaur_66831


      • ML Zoomcamp: https://github.com/alexeygrigorev/mlbookcamp-code/tree/master/course-zoomcamp

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