4 articles on causal analysis.
Causal Analysis
Governance failures are not random events. They are the downstream consequences of structural gaps that were present long before the failure became visible. Most governance processes are designed to respond to the failure. Very few are designed to diagnose what made it possible.
Causal Analysis
Most governance uses regression: explaining what has already happened. The more valuable and underused mode is forward projection: anticipating what will happen if a decision or system performs as designed. The distinction determines whether a board governs proactively or retrospectively.
Causal Analysis
Causal analysis is not a methodology reserved for data scientists and network engineers. It is a reasoning discipline that executives can learn, and boards can apply, to governance decisions. Here is where to start.
Causal Analysis
Most governance failures are diagnosed at the symptom level. Causal analysis is the methodology that finds the structural failure three or four steps upstream. Boards that apply it to AI governance govern better.