Ethnic Bias in Prediction and Decision Making Algorithms in Precision Psychiatry: Challenges in a Shrinking World

Abstract

Abstract This article examines ethnic bias in predictive algorithms and decision-making systems within precision psychiatry. While these models aim to provide individualised outcomes and improve healthcare, they often perpetuate historical biases present in training datasets. Factors such as historical disparities, poor access to care, and ethnic under-representation in data collection exacerbate these biases, leading to inequitable healthcare predictions and decisions that affect outcomes for ethnic minority groups. The article emphasizes the necessity of understanding the causal relationships underlying data patterns to mitigate some of these biases and enhance the effectiveness and fairness of machine learning applications in mental health care.

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