Fairness in Predictive Modeling: Addressing Gender Bias in Income Prediction through Bias Mitigation Techniques

When machine learning algorithms are used in delicate areas, such as income prediction, they frequently reinforce preexisting societal prejudices and produce biased results based on racial, gender, or other demographic characteristics.Methods for reducing gender bias in an income prediction model trained using the Adult Income Dataset were examined in this study.To account for biases, we use a fairness constraint technique called the Exponentiated Gradient with Demographic Parity, which aims to treat both genders equally without causing a large drop in predictive accuracy.These findings indicate that while subtler biases may still exist, fairness-aware techniques can reduce bias.The results highlight the necessity of sophisticated bias-mitigation techniques that integrate pre-, in-, and postprocessing techniques to handle intricate fairness concerns in a range of applications.