How will AI and ML be standardized in filtering?

AI and ML standardization in filtering is challenging because providers treat their algorithms as competitive advantage and security protection. Too much transparency enables spammers to game systems.

What may emerge is standardized feedback mechanisms: clearer communication about why filtering occurred and what senders can do to improve. Standards for sender dashboards, error messages, and reputation indicators could develop.

Ethical AI principles may influence filtering standards. Transparency, fairness, and accountability concerns could drive requirements for explainability or bias monitoring in filtering systems.