Bias Mitigation: Debiasing, CDA, and Fair Fine-tuning › Summary

Bias mitigation addresses demographic disparities in language model behavior across three intervention layers.

Data-level interventions include corpus filtering (removing or reweighting biased documents), counterfactual data augmentation (generating demographic-swapped counterparts), and re-sampling (adjusting example weights or frequencies to balance demographic representation). These are the cheapest interventions and most effective when you have control over training data. Corpus reweighting is generally preferable to deletion because it preserves the original data for analysis and makes experiments easier to reverse. CDA provides the cleanest statistical guarantee: by pairing every gendered training example with its demographic counterpart, it makes gendered terms statistically uninformative as predictive features.

Model-level interventions modify the representations themselves. Hard debiasing identifies the bias subspace via PCA on definitionally gendered pairs and projects neutral words orthogonal to it, removing the geometric basis for stereotypical associations without altering the semantic content of the embeddings. Soft debiasing incorporates a fairness penalty into the training objective, simultaneously optimizing task performance and demographic neutrality. Adversarial training adds a competing adversary network that prevents demographic information from encoding in the learned representations, using gradient reversal to turn the adversary's signal against itself. Counterfactual fine-tuning penalizes prediction changes across demographic input variants using KL divergence, directly optimizing for the consistency that counterfactual fairness requires. RLHF with fairness rewards shapes the full generative distribution through human preference data, making the model reason differently rather than just avoid specific words.

Inference-time interventions require only API access: system prompt instructions, few-shot demographic-balanced exemplars, and chain-of-thought fairness auditing. These are easy to deploy but less reliable than training-time fixes. They are most valuable as a first deployment layer while deeper mitigations are developed, and they create an auditable record of fairness intent even when they cannot guarantee fairness outcomes.

Every mitigation technique requires rigorous evaluation: task performance must not degrade unacceptably, bias metrics must improve on the targeted dimensions, and counterfactual consistency should increase. The next chapter on Fairness Metrics provides the formal measurement framework you need to close this evaluation loop.

Mitigation is not a one-time operation. It is an ongoing commitment to measuring, intervening, and remaining accountable for the disparate impacts that language models can create. The technical toolkit covered in this chapter provides the means; the organizational discipline to apply it consistently over time is what produces equitable systems.