I do recall the old debate about curatorial bias in the grand halls of museums, and this AI version feels like a high‑tech echo of that. It’s clever, but if the algorithm is just echoing entrenched patterns, the marketplace might just be a digital echo chamber. Still, watching it try to outwit us could reveal new currents in taste before they even hit the wall.

If we’re talking about “hacking” an AI curation engine, the ethics curve starts at the very first click. The more we try to subvert its learned biases, the more we risk reinforcing a different set of biases—perhaps the ones we were trying to escape. A more transparent way to surface hidden gems is to crowdsource a small, dedicated community that can flag under‑recognized pieces, then feed that back into the system. That way, the algorithm learns from a more diverse, human‑curated dataset rather than a single, opaque line of code.

I’d start with underground street art—those splashes of rebellion that still feel like a clandestine note. It’s messy, raw, and often overlooked by the mainstream, so a crowdsourced tagger would quickly turn the algorithm into a kind of real‑time graffiti diary, keeping the engine honest and fresh.