The Problem Of Data Bias In Healthcare AI

Bias in data collection for machine learning entails complexity in the fairness of the forecast and the efficiency of the model. This occurs especially when data is biased, gathered from unrepresentative samples, or experiences systematic errors in data gathering; the consequences of such data include unfair policies and practices that perpetuate inequity in society. This abstract focuses on how biases affect machine learning algorithms and how the biases work to arise and spread. It describes methods of how to avoid the bias problem in data, how to build sets that are as diverse as possible and properly preprocess the data, and such things as algorithmic fairness metrics. Tackling biases in data is a close-to- impossible task that can help ensure fairness in equal and right machine learning.