Geographical Representativeness

Geographical representativeness in datasets focuses on ensuring that data accurately reflects the distribution of characteristics across different geographical locations, avoiding biases that skew results or limit generalizability. Current research emphasizes developing methods for adaptive data collection and sampling to improve representativeness, particularly in areas like biomedical studies and AI model training, often employing techniques like fair clustering and active learning algorithms. Addressing this issue is crucial for mitigating algorithmic bias, enhancing the reliability of AI systems, and ensuring equitable outcomes across diverse populations in various applications, from healthcare to social media moderation. The development and application of metrics to quantify geographical representativeness are also active areas of investigation.