CaRE SD

CaRE (and its variations, such as CARE-SD and GenderCARE) encompasses research efforts focused on improving the fairness, reliability, and efficiency of AI systems across diverse applications. Current research emphasizes developing robust benchmarks and evaluation metrics to assess bias and trustworthiness, alongside the creation of novel algorithms and model architectures (e.g., classifier-based approaches, Bayesian kernel inference) for bias mitigation and improved performance. This work is significant for advancing responsible AI development, particularly in sensitive domains like healthcare and social robotics, by promoting fairness, accuracy, and transparency in AI-driven decision-making.