Bias in AI: Algorithms as Perpetrators and Mitigators of Inequality in the Medical Field

Bias in AI: Algorithms as Perpetrators and Mitigators of Inequality in the Medical Field

The development of artificial intelligence relies on data that is collected from multiple fields, which is used to generate models that increase understanding of complex events. Intelligent algorithms promise faster and more accurate solutions but how they learn from data can unintentionally reflect societal and cultural biases, which creates serious consequences for domains such as healthcare. The growing use of AI-based technologies increases concerns about the perpetuation of these imbalances, which is commonly referred to as data bias.
Bias can occur at various stages of AI development, including feature selection, algorithm design and outcome presentation, each of which can influence the model’s inferences and decisions. Biased AI algorithms can have consequences in the medical field in terms of misdiagnoses and wrong treatments. Methods to mitigate bias in AI include data-centric solutions, computational solutions and recalibration solutions and combinations of these strategies. However, these solutions address future research and modelling, rather than existing models, but they may create fairer AI. Other methods, such as word embedding, are used to optimize existing datasets and to produce less biased algorithms.
Machine learning models can be contributors to and solutions for data discrimination. By developing fairness-aware algorithms and refining models to reflect the full diversity of human experience, AI can address the biases that are inherent in its own design. Ethical oversight and accountability are essential to the development of intelligent algorithms to ensure that these technologies serve all individuals equitably.