Data Debiasing

Data debiasing aims to mitigate biases in machine learning models, stemming from skewed training data, to ensure fair and accurate predictions across different subgroups. Current research focuses on developing novel algorithms and model architectures, such as those employing moment-constrained learning, contrastive clustering, and post-processing techniques like selective debiasing, to effectively remove or reduce bias while preserving model performance. These efforts are crucial for improving the reliability and trustworthiness of AI systems across various applications, from recommender systems and large language models to healthcare and social sciences, where biased predictions can have significant real-world consequences.