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7.3. How Do You Keep the System Aligned After Launch?

How Do You Keep the System Aligned After Launch?

“Trust isn’t a one-time review. It’s a relationship you maintain, or lose.”

AI systems don’t stand still : They learn. They drift. They adapt to new data, shift with emerging trends, and evolve under real-world pressure. Yet the oversight meant to govern them too often stays frozen in time.

Risk boundaries that made sense at launch can quickly become outdated. Thresholds calibrated for yesterday’s environment may fail to detect today’s harms. And even when users provide feedback, it too often fails to reach the teams responsible for improving the system, trapped in disconnected logs, ignored inboxes, or unstructured channels.

For example, let consider a major financial institution deployed an AI model in 2020 to evaluate small business loan applications. At launch, the system passed fairness and performance audits using pre-pandemic training data. But as the economy shifted and gig economy workers became a larger portion of applicants, the model failed to adapt.

By 2023, the approval rate for self-employed applicants had dropped by 38%, but no one noticed.

Why? : Because oversight was still focused on traditional metrics: accuracy, uptime, latency, not representational fairness or user drift.

There was no monitoring process for fairness over time, No trigger thresholds for population change, and No assigned reviewer with the mandate to investigate long-term trust decay.

This section explores how to govern AI systems that continue to learn, drift, and operate under changing conditions. Because while most modern AI is engineered for adaptation, most oversight frameworks are engineered for stasis.

We focus on three dimensions of post-deployment trust that are frequently overlooked, but increasingly critical:

  • Monitoring what actually changes: not just performance scores, but fairness, representational relevance, and ethical alignment over time
  • Designing feedback systems that lead to real revisions: ensuring that complaints and corrections don’t just vanish into inboxes
  • Defining who has the authority, and responsibility, to approve model evolution: and how that role is governed

This chapter asks not just how we review AI at launch, but how we remain accountable to it as it grows.