Strategies for Mitigating Gender Bias in AI Models

Mitigating gender bias requires a multifaceted approach, starting from the initial stages of data collection and extending to model training and evaluation. Employing diverse and inclusive data collection practices, utilizing debiasing techniques during preprocessing, and implementing fairness-aware machine learning algorithms are crucial steps. Additionally, continuous monitoring for bias post-deployment ensures that models remain fair and equitable over time, adjusting as necessary to address any emerging biases.