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Responsible ML

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.

Daron Yondem

April 24, 2021

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  1. Responsible ML
    Daron Yöndem

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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
    • 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

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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

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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;

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