Assessing Ethical Risks of Data-Driven Prediction Models in Healthcare AI

Abstract

The use of artificial intelligence (AI) to improve predictive modeling of healthcare has increased disease detection, planning of treatment, and risk assessment, but it also raises ethical issues concerning bias, fairness, transparency, and privacy of data. This paper analyses these concerns by comparing a traditional machine learning model with an ethical-enhanced model on a diabetes data. The model that was created with no bias-reducing measures had the accuracy of 75.76%. Comparatively, the ethically enhanced model, which contained the fairness-oriented technique, including the Synthetic Minority Over-sampling Technique (SMOTE), achieved a precision of 7 5. 3 2%. Even though the improved model demonstrated slight decrease in the overall accuracy, it performed greatly improvements in recall among diabetic patients (74% vs. 66%), which minimized the risk of false diagnosis. Analysis of feature importance was done to enhance model transparency. The findings emphasize the need to incorporate fairness-based concepts of healthcare AI to reduce bias and improve ethical reliability and competitive predictive accuracy.

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