Mitigating Bias

Mitigating bias in artificial intelligence focuses on developing methods to ensure fairness and equity in AI systems, addressing disparities arising from biased training data and algorithmic design. Current research emphasizes techniques like adversarial training, data augmentation, and algorithmic modifications (e.g., neuron pruning, contrastive learning) applied to various model architectures, including large language models, convolutional neural networks, and quantum machine learning models. This work is crucial for ensuring responsible AI development, promoting equitable access to AI benefits, and preventing discriminatory outcomes in diverse applications such as healthcare, education, and criminal justice.