A team building a generative model wants to reduce demographic bias that originates in the training corpus itself rather than correcting it after generation. In principle, which characteristic defines a data-level mitigation as opposed to an inference-time mitigation?
- AIt applies a fixed post-processing filter that rewrites each generated output to remove sensitive terms before the response is returned to the user.
- BIt adjusts the decoding temperature and top-p sampling at generation time so that lower-probability tokens describing under-represented groups appear more often.
- CIt rebalances, reweights, or curates the training examples so that the distribution the model learns from is more representative before any training begins. Correct
- DIt inserts a runtime safety classifier that blocks any response whose content the classifier scores as demographically skewed.
Why A is wrong: Tempting because it reduces visible bias, but rewriting outputs happens at inference time and leaves the learned representation untouched, so it is not a data-level mitigation.
Why B is wrong: Tempting because sampling changes outputs, but temperature and top-p operate during inference and do not change what the model learned, so they are not data-level fixes.
Why C is correct: Correct: data-level mitigations act on the corpus, through rebalancing, reweighting, or curation, so the model never internalises the skew in the first place.
Why D is wrong: Tempting because classifiers govern outputs, but a runtime gate is an inference-time guardrail acting on finished generations, not an intervention on the training data.