Causes and Countermeasures of Algorithmic Bias and Health Inequity

Abstract

With the development of information technology, artificial intelligence shows great potentials for clinical diagnosis and treatment. Nevertheless, bias in algorithms derived by artificial intelligence can lead to problems such as unequal distribution of healthcare resources, which significantly affect patients' health equity. Algorithmic bias is a technical manifestation of human bias, whose formation strongly correlates with the entire development process of artificial intelligence, starting from data collection, model training and optimization to output application. Healthcare providers, as the key direct participants in ensuring patients' health, should take corresponding measures to prevent algorithmic bias to avoid its related health equity issues. It is important for healthcare providers to ensure the authenticity and unbiasedness of health data, optimize the fairness of artificial intelligence, and enhance the transparency of its output application. In addition, healthcare providers need to consider how to tackle bias-related health inequity, so as to comprehensively ensure patients' health equity. In this study, we reviewed the causes and coping strategies related to algorithmic bias in healthcare, with the aim of improving healthcare providers' awareness and ability to identify and address algorithmic bias, and laying a foundation for ensuring patients' health equity in the information age.

Keywords

• artificial intelligence|algorithmic bias|health equity|human bias|information