De Bias

Debiasing focuses on mitigating unwanted biases in machine learning models, stemming from skewed training data or inherent algorithmic limitations. Current research emphasizes developing methods to detect and reduce biases in various model types, including large language models (LLMs) and graph neural networks (GNNs), often employing techniques like contrastive learning, instruction tuning, and knowledge distillation to achieve fairness across different demographic groups. This work is crucial for ensuring the responsible development and deployment of AI systems, preventing discriminatory outcomes in applications ranging from loan approvals to facial recognition. The ultimate goal is to create more equitable and reliable AI systems that avoid perpetuating societal biases.