Responsible ML

This is a 30 minutes presentation I did for Global AI Nights Istanbul where I introduced the concept of Responsible ML and talked about projects such as Fairlearn, InterpretML, and SmartNoise.

Transcript

1. Responsible ML
   Daron Yöndem
   http://daron.me
   @daronyondem
   

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2. The What and the Why?
   • Fairness: ML models may behave unfairly by negatively impacting
   groups of people, such as those defined in terms of race, gender,
   or age.
   • Interpretability: Ability to explain what parameters are used and
   how the models “think” to explain the outcome for regulatory
   oversight.
   • Differential Privacy: Monitoring applications’ use of personal data
   without accessing or knowing the identities of individuals
   

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7. Fairlearn
   • Fairness Assessment
   • Fairness Mitigation (in Classification and Regression Models)
   • During or after model building.
   • Open Source https://github.com/fairlearn
   • Integrated into Azure Machine Learning
   

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8. Fairlearn
   Demo
   

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9. InterpretML
   • Glassbox Models (Decision trees, rule lists, linear models,
   Explainable Boosting Machine)
   • Blackbox Models (Existing model)
   • Explanations are approximations
   • Open Source https://github.com/interpretml
   

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10. InterpretML
    Demo
    

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11. Differential Privacy
    The guarantee of a differentially private algorithm is that its behavior
    hardly changes when a single individual joins or leaves the dataset.
    • Smart Noise https://smartnoise.org/
    This toolkit uses state-of-the-art differential privacy (DP)
    techniques to inject noise into data, to prevent disclosure of
    sensitive information and manage exposure risk.
    

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12. Links worth sharing
    Microsoft Learn : Explore differential privacy
    • https://drn.fyi/2QRL3V1
    Capgemini “AI and the ethical conundrum” Report
    • https://drn.fyi/3gBsz5G
    IDC report: Empowering your organization with Responsible AI
    • https://drn.fyi/3sKilCx
    

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13. Thanks
    http://daron.me | @daronyondem
    Download slides here;
    http://daron.me/decks
    

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