The Risks of Excluding the Disengaged From Your Dataset

Explore the critical importance of inclusive datasets in machine learning analysis through this 38-minute conference talk from the Toronto Machine Learning Series. Discover how excluding disengaged populations can significantly impact the reliability of predictions in various fields, including elections, consumer demand, and pandemic trajectories. Learn from Danielle Goldfarb, Vice President and General Manager of Global Affairs, Economics and Public Policy at RIWI, as she delves into the potential pitfalls of relying solely on big data without considering the inclusivity of the underlying information. Gain valuable insights on how to ensure your datasets are robust and representative, ultimately leading to more accurate and meaningful predictions in your machine learning projects.