Responsible and Explainable AI - Supreet Kaur

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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