Demographic Bias

Demographic bias in artificial intelligence (AI) systems, particularly large language models (LLMs) and machine learning models, focuses on identifying and mitigating unfair or discriminatory outcomes based on protected characteristics like gender, race, and age. Current research utilizes various techniques, including debiasing layers, specialized datasets, and fairness-aware training methods, often applied to models such as convolutional neural networks and decision trees, to analyze and reduce these biases across diverse applications like healthcare, facial recognition, and job recommendations. Addressing demographic bias is crucial for ensuring fairness, equity, and trust in AI systems, impacting both the scientific understanding of algorithmic fairness and the ethical deployment of AI in real-world settings.