The Importance of Model Fairness and Interpretability in AI Systems

Machine learning model fairness and interpretability are critical for data scientists, researchers and developers to explain their models and understand the value and accuracy of their findings. Interpretability is also important to debug machine learning models and make informed decisions about how to improve them. In this session, Francesca will go over a few methods and tools that enable you to "unpack†machine learning models, gain insights into how and why they produce specific results, assess your AI systems fairness and mitigate any observed fairness issues.
Using open source fairness and interpretability packages, attendees will learn how to:
Explain model prediction by generating feature importance values for the entire model and/or individual datapoints.
Achieve model interpretability on real-world datasets at scale, during training and inference.
Use an interactive visualization dashboard to discover patterns in data and explanations at training time.
Leverage additional interactive visualizations to assess which groups of users might be negatively impacted by a model and compare multiple models in terms of their fairness and performance.

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