biasmitigation

Bias mitigation refers to the set of methods aimed at reducing biased outcomes in data-driven systems. It covers practices from data collection and preprocessing to model development and deployment, with the goal of limiting disparate impact on protected groups.

Bias can arise from historical inequities reflected in data, model objectives that prioritize accuracy over fairness, and feedback effects during deployment that shift data distributions.

Mitigation strategies are commonly grouped into pre-processing, in-processing, and post-processing approaches. Pre-processing methods modify the data to reduce bias before learning, using techniques such as reweighting, resampling, or removing sensitive attributes in a way that preserves predictive utility. In-processing methods embed fairness considerations into the learning algorithm, for example by adding fairness constraints, regularizers, or using adversarial debiasing. Post-processing methods adjust model outputs to satisfy fairness criteria after training, such as threshold adjustments or calibration across groups.

Fairness criteria include demographic parity, equalized odds, equal opportunity, and calibration; practitioners typically weigh these against overall predictive performance.

Evaluation hinges on subgroup-level metrics, robustness to distribution shift, and cross-group generalization. Applications span hiring, lending, criminal justice, medicine, and advertising, where biased decisions can have serious consequences.

Challenges include conflicting fairness definitions, data quality limits, interpretability, scalability, and governance considerations, as well as legal and ethical constraints. Bias mitigation is an ongoing process requiring auditing, documentation, and stakeholder engagement to reflect context and evolutions in data and society. See also algorithmic fairness, responsible AI.