Engineering, production, governance, cost, communication · insight-0296
Pair any model-driven KPI target with guardrail metrics to resist Goodhart effects
- Evidence type
- practitioner heuristic
- Difficulty
- medium
- Roles
- Data scientist, Data analyst, ML engineer
- Industries
- telecom, e-commerce, saas
Summary: situation, action, reason, boundary
Situation
A call-centre routing model is rewarded on average handle time, and handle time falls while repeat calls rise.
Action
Define the goal metric together with guardrails that catch likely gaming paths (repeat contacts, complaints, revenue per customer), review them together, and rotate or audit metrics when optimisation pressure is high.
Reason
Optimising a proxy beyond a point can make it ineffective or harmful through several mechanisms; guardrails reveal the damage early.
Boundary
Operational KPIs, model objectives, incentive schemes, experiment success metrics.
Symptom or risk
Once a metric becomes a target, teams and models optimise the metric rather than the goal, and side effects grow unnoticed.
Exceptions
When the metric is the goal itself (for example energy consumed), gaming risk is lower.
Trade-offs
More metrics dilute focus; keep a small set with one primary.
Pitfalls
Adding guardrails but never letting them block a launch.
Lessons and concepts
Claims and their sources
Manheim and Garrabrant describe several distinct mechanisms by which optimising a metric makes it ineffective or harmful (Goodhart's law).
Kohavi, Tang and Xu discuss experiment metrics including an overall evaluation criterion and guardrail metrics.