Regarding accurate labels, a lack of appropriate or sufficient labeling will knock over your model regardless of how powerful your model is (interpretable or not). This is where other benefits of model interpretation come in—you can spot potential errors in your model's training, and that gives you an indication that you need to re-evaluate your base assumptions about the data.

SHAP is cool technology! It looks like it builds off LIME and similar to fit a hyperplane against the model surface. I'm not surprised the article didn't mention it though, as it's a bit in the weeds for an overview piece.