What AI companies do.
Not what they say.

What are AI governance regulations?

AI governance regulations are legal frameworks and policies designed to control how artificial intelligence systems are developed, deployed and monitored. The EU AI Act is the most comprehensive example — it classifies AI systems by risk level and imposes requirements on transparency, human oversight and accountability. In the US, regulations are fragmented across sectors. BrokenCtrl documents the gap between what these regulations require and what companies actually do.

What is AI governance and why does it matter?

AI governance is the set of rules, principles and institutional structures that determine how AI systems are built and used. It matters because AI is being deployed in decisions that affect people's lives — hiring, lending, law enforcement, military targeting — often without meaningful accountability. When governance fails, the people harmed have no recourse. BrokenCtrl documents those failures with sourced evidence.

What are the best practices for AI governance?

The most cited AI governance best practices include mandatory transparency about how systems work, independent auditing of high-risk deployments, human oversight requirements for consequential decisions, clear accountability chains when systems cause harm, and public disclosure of incidents. The problem BrokenCtrl documents is that most companies adopt these as voluntary commitments — which means they disappear when they become commercially inconvenient.

What is the difference between AI ethics and AI governance?

AI ethics refers to the principles that should guide AI development — fairness, transparency, accountability, safety. AI governance refers to the mechanisms that actually enforce those principles — laws, audits, regulatory bodies, contractual requirements. Ethics without governance is a press release. Governance without ethics is compliance theatre. BrokenCtrl covers both — and the gap between them.