Deployment feedback loop amplifying allocation bias | AIGP
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Deployment feedback loop amplifying allocation bias: Which concept most decisively explains the observed

AIGP Understanding How to Govern AI Development Medium

The model changes outcomes that become future data, reinforcing its initial allocation pattern.

The question

A retail demand model allocates inventory using prior store sales. Stores receiving less inventory then record fewer sales, and those outcomes feed the next training cycle. Which concept most decisively explains the observed pattern?

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  1. Static sampling bias in sales records
    Sampling bias may exist in the historical records, but it does not describe the model changing later sales observations.
  2. Ordinary concept drift caused by changing customer demand patterns
    Concept drift concerns changes in the underlying relationship between inputs and outcomes; the stated mechanism is the model influencing the outcomes.
  3. Label leakage caused by inventory records revealing future sales targets during training
    Label leakage involves inappropriate access to target information during training, not allocation decisions suppressing later target values.
  4. Deployment feedback loop amplifying allocation bias ✓
    The allocation decision changes subsequent sales data, which then reinforces the original allocation pattern during retraining.
The trap
Trace whether predictions influence the outcomes later used for retraining; that causal loop is decisive.

How to remember it

The model changes outcomes that become future data, reinforcing its initial allocation pattern.

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