635: The Perils of Manually Labeling Data for Machine Learning Models
Super Data Science: ML & AI Podcast with Jon Krohn
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The Perils of Manually Labeling Data for Machine Learning Models
The chapter delves into the risks associated with manual data labeling in machine learning, highlighting the dangers of encoding societal biases into models. It introduces Watchful as a solution offering predictive heuristics to ease data labeling and bias mitigation, also mentioning the favorable attributes of the spacebacks command line editor.
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