The development of AI is creating new opportunities to improve the lives of people around the world. It’s also raising new questions about the best way to build fairness, interpretability, privacy, security, and other moral and ethical values into these systems.
Using the Jupyter Notebook and high-level TensorFlow APIs, Andrew Zaldivar shares hands-on examples that highlight current work and recommended practices toward building AI systems that are fair and inclusive for all. You’ll learn how to design your model using concrete goals for fairness and inclusion, the importance of using representative datasets to train and test models, how to check a system for unfair biases, and how to analyze performance. Each of these points will be accompanied by a technical demonstration that is readily available for you to try for yourself.
Andrew Zaldivar is a developer advocate in the AI Group at Google, where he’s helping to bring the benefits of AI to everyone by developing, evaluating, and promoting tools and techniques that can help the larger communities build responsible AI systems. Previously, Andrew was a senior strategist in Google’s Trust and Safety Group and worked on protecting the integrity of some of Google’s key products by utilizing machine learning to scale, optimize, and automate abuse-fighting efforts. He holds a PhD in cognitive neuroscience from the University of California, Irvine, and was an Insight Data Science fellow.
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