Reducing Model Bias
Building effective data products requires understanding the needs of data scientists who often face busywork that distracts from their core tasks. By asking the right questions and identifying underlying issues, designers can help streamline processes and reduce bias in models. The key lies in uncovering the real pain points, like financial losses, to drive meaningful improvements.In this clip
From this podcast

Super Data Science: ML & AI Podcast with Jon Krohn
658: How to Build Data and ML Products Users Love — with Brian T. O'Neill
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