Fine-tuning does not ensure current facts: What specific risk remains?
Fine-tuning changes learned behavior, but it does not keep quarterly-changing employment criteria current.
The question
An employment-screening team fine-tunes a model on its preferred explanation style and historical screening examples. Employment criteria and role requirements change quarterly, but retraining occurs annually. What specific risk remains?
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- Fine-tuning should be paired with annual fairness testing and expanded historical examples.Testing and data expansion may help, but annual review still fails to address quarterly changes in current screening requirements.
- Fine-tuning can reduce stylistic consistency across screening outputs.Fine-tuning is generally intended to shape behavior and style; the explicit concern is changing factual requirements, not consistency alone.
- Fine-tuning may increase model hosting costs.Hosting cost is possible, but the stated quarterly-change and annual-retraining facts point to stale screening information as the decisive risk.
- Fine-tuning does not ensure current facts. ✓Fine-tuning can change learned behavior and style, but annual retraining may leave the model misaligned with current criteria and role requirements.
The trap
Ask whether the proposed method changes behavior or supplies current facts; fine-tuning alone does not establish freshness. How to remember it
Fine-tuning changes learned behavior, but it does not keep quarterly-changing employment criteria current.
How many of these would you get right?
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