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"Fairness in data and machine learning algorithms is critical to building safe and responsible AI systems. While accuracy is one metric for evaluating the accuracy of a machine learning model, fairness gives you a way to understand the practical implications of deploying the model in a real-world situation.
In this code pattern, you use a diabetes data set to predict whether a person is prone to have diabetes. You'll use IBM Watson® Studio, IBM Cloud Object Storage, and the AI Fairness 360 Toolkit to create the data, apply the bias mitigation algorithm, then analyze the results."Original code pattern