The development of AI is creating new opportunities to improve the lives of people around the world. It is also raising new questions about the best way to build fairness, interpretability, privacy, security, and other moral and ethical values into these systems.
Using Jupyter Notebook and high-level TensorFlow APIs, this presentation will share hands-on examples that highlight current work and recommended practices towards building AI systems that are fair and inclusive for all. Throughout the presentation, we will discuss 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 the attendees to try for themselves.
This is all in an effort to share knowledge, research, tools, datasets, and other resources with the larger community so that together we can evolve AI towards positive goals.
Andrew Zaldivar is a Developer Advocate in Google’s AI group, helping to bring the benefits of AI to everyone. Andrew develops, evaluates and promotes tools and techniques that can help the larger communities build responsible AI systems.
Before joining Google AI, Andrew was a Senior Strategist in Google’s Trust & 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.
Prior to joining Google, Andrew completed his Ph.D. in Cognitive Neuroscience from the University of California, Irvine and was an Insight Data Science fellow.
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