Bias Mitigation

Bias Mitigation

Bias mitigation refers to the actions taken to reduce unfair or unequal outcomes caused by data, processes, or decision-making systems. Bias can appear in hiring, lending, medical care, or any place decisions affect people, often because of incomplete data, historical inequalities, or assumptions built into algorithms. Mitigation involves identifying where unfairness exists, changing data or models to correct it, and testing to ensure improvements actually work. Techniques include balancing datasets, adjusting how models weigh different inputs, introducing human review steps, and making decisions more transparent. The goal is not to make everyone identical, but to ensure choices are based on relevant, fair criteria. This work matters because biased decisions can lock people out of jobs, housing, or services and deepen social inequalities. Reducing bias builds trust in systems and helps organizations meet legal and ethical standards. It also improves accuracy by ensuring models learn from representative data. Because bias can reappear over time, mitigation is an ongoing process of monitoring, updating, and involving diverse perspectives. In short, bias mitigation helps create fairer, more reliable decisions that treat people equitably.