Bias mitigation refers to the strategies and techniques used to reduce or eliminate bias in various contexts, such as decision-making, data analysis, and machine learning. Bias can lead to unfair treatment or inaccurate results, so addressing it is crucial for promoting fairness and equity. Common methods of bias mitigation include diversifying training data, implementing fairness algorithms, and conducting regular audits to identify and correct biased outcomes. These approaches aim to ensure that systems and processes are more inclusive and representative of diverse populations, ultimately leading to better and more equitable results in areas like artificial intelligence and human resources.