Maybe not the hardest, but still challenging. Unknown biases in training data are a challenge in any experimental design. Opaque ML frequently makes them more challenging to discover.

The most serious thing I heard of was Sotomayor's staff intimating to universities bringing the Justice in to speak hadn't purchased enough copies of her book.

Can't wait for the calls of "But her books!"

I wonder if there will be an anti SLAPP action soon from Cohen's team. (Not sure what the rules are in that jurisdiction.)

Exactly.

The general approach is to use interpretable models where you can understand how the model works and what features it uses to discriminate, but that doesn't work for all ML approaches (and even when it does our understanding is incomplete.)

Unless you're in Tibet, Xinjiang, or another place observing UTC+8 with a significant offset from local solar time.

I've had great luck with Upton teas for the past decade or more in the US. Great selection, teas at multiple price points from broken leaf to first flush single estate.

They also make NEMA plugs with threaded rings around the boot for applications in marine and other harsh environments.