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MIT-Harvard Communications Information Networks Circuits and Signals (CINCS) / Hamilton Institute Seminar
Abstract:
Machine learning powers many advanced search and recommendation systems, and user experience strongly depends on how well ML systems perform across all data segments. This performance can be impacted by biases, which can lead to a subpar experience for subsets of users, content providers, or applications. Biases may arise at different stages in machine learning systems, from existing societal biases in the data, to biases introduced by the data collection or modeling processes. These biases may impact the performance of various components of ML systems, from offline training, to evaluation and online serving in production systems. Specific techniques have been developed to help reduce bias at each stage of an ML system. We will describe sources of bias in ML technology, why addressing bias matters, and techniques to mitigate bias, with examples from our Inclusive AI work at Pinterest for search and recommendations. Mitigating bias in machine learning systems is crucial to successfully achieve our mission to "bring everyone the inspiration to create a life they love".
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