Use reinforcement learning shaped by employee ratings | AIGP
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Use reinforcement learning shaped by employee ratings: Which approach best fits this constraint?

AIGP Understanding the Foundations of AI Governance Medium

Ratings tied to actions and outcomes provide reward feedback suitable for reinforcement learning.

The question

An internal employee assistant recommends response strategies. The organization cannot label enough examples, but employees can rate recommendations after trying them, and the system can learn from those feedback signals. Which approach best fits this constraint?

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  1. Use reinforcement learning shaped by employee ratings and response outcomes. ✓
    Reinforcement learning is designed to improve actions using reward feedback, matching ratings and outcomes when sufficient labels are unavailable.
  2. Cluster employee questions to discover recurring request categories.
    Unsupervised clustering can reveal structure in questions but does not optimize response strategies from employee evaluations.
  3. Deploy a fixed ruleset and have supervisors revise response rules after reviewing periodic feedback reports.
    This can provide controlled manual improvement, but the system itself does not learn response strategies from the available interaction-based reward signals.
  4. Train a supervised classifier from the limited labeled sample.
    Supervised learning uses labeled examples and therefore does not make primary use of the available ratings and outcome feedback.
The trap
Feedback evaluating actions or outcomes is a reward signal, not merely an input label or an unsupervised cluster.

How to remember it

Ratings tied to actions and outcomes provide reward feedback suitable for reinforcement learning.

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