The article "The Sociodemographic Biases in Machine Learning Algorithms: A Biomedical Informatics Perspective" by Gillian Franklin et al. explores the biases inherent in machine learning (ML) algorithms used for health care risk assessment. These biases, often rooted in sociodemographic characteristics such as race, ethnicity, gender, age, and socioeconomic status, can lead to inequities and discrimination in healthcare delivery. The authors highlight the impact of erroneous electronic health record (EHR) data and the potential drawbacks of training data and algorithmic biases in large language models (LLMs). They outline various types of biases, including implicit bias, selection bias, and algorithmic bias, and propose recommendations for improving LLM training data and de-biasing techniques. The article emphasizes the need for inclusive and fair representation in training datasets to ensure that AI models can effectively serve diverse populations and avoid perpetuating existing healthcare disparities. The authors also discuss the limitations of current ML models and the challenges in controlling biases, particularly in the context of large language models. They advocate for best practices in model development, including strong data governance, diverse training populations, and continuous monitoring of model performance to address and mitigate biases.The article "The Sociodemographic Biases in Machine Learning Algorithms: A Biomedical Informatics Perspective" by Gillian Franklin et al. explores the biases inherent in machine learning (ML) algorithms used for health care risk assessment. These biases, often rooted in sociodemographic characteristics such as race, ethnicity, gender, age, and socioeconomic status, can lead to inequities and discrimination in healthcare delivery. The authors highlight the impact of erroneous electronic health record (EHR) data and the potential drawbacks of training data and algorithmic biases in large language models (LLMs). They outline various types of biases, including implicit bias, selection bias, and algorithmic bias, and propose recommendations for improving LLM training data and de-biasing techniques. The article emphasizes the need for inclusive and fair representation in training datasets to ensure that AI models can effectively serve diverse populations and avoid perpetuating existing healthcare disparities. The authors also discuss the limitations of current ML models and the challenges in controlling biases, particularly in the context of large language models. They advocate for best practices in model development, including strong data governance, diverse training populations, and continuous monitoring of model performance to address and mitigate biases.