Inspect reward traces across representative scenarios: Before | AIGP
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Inspect reward traces across representative scenarios: Before approval, which evidence most directly tests

AIGP Understanding the Foundations of AI Governance Medium

Reward traces reveal how the learning process values competing scheduling objectives.

The question

A logistics company’s scheduling agent learns from reward feedback. Managers suspect the reward function favors short-term punctuality over safe workload distribution. Before approval, which evidence most directly tests that concern?

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  1. Ask the system designer to provide a narrative explaining the intended balance between punctuality and workload safety.
    The designer’s account states intended objectives but does not show what the implemented reward process actually values in representative cases.
  2. Compare average punctuality with the incumbent scheduler across the same planning period.
    A punctuality comparison measures one outcome and cannot reveal whether the reward design undervalues safe workload distribution.
  3. Inspect reward traces across representative scenarios, including objective components and tradeoffs.
    Reward traces show which behaviors receive value and whether punctuality is being favored over safe workload distribution.
  4. Review historical schedules labeled as successful or unsuccessful.
    These labels describe schedule outcomes but do not show how the reward function values punctuality against workload safety.
The trap
For reward-based systems, inspect implemented rewards and tradeoffs, not only outcomes or designer intent.

How to remember it

Reward traces reveal how the learning process values competing scheduling objectives.

How many of these would you get right?

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