Automated Solutions and Human Judgement – Both Crucial for AI Training
Summary
This article explains why AI training depends on both automation and human judgment. It highlights data collection, deduplication, normalization, labeling, and validation as core steps in building usable training datasets. It argues that automation scales these workflows, but human reviewers remain essential for ambiguous cases, edge conditions, and bias reduction. The piece also shows how poor data choices can create harmful model outcomes in healthcare and other sensitive domains. It concludes that reliable AI systems need both machine-driven efficiency and expert oversight.
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