Understanding Bias in Machine Learning Models

Model bias, a systematic error from erroneous assumptions in machine learning algorithms, is a significant concern for AI developers and organizations using ML technology. It can lead to poor customer experience, profitability loss, or even fatal misdiagnoses if not addressed. To prevent biases at various stages of the machine learning pipeline, it's crucial to identify, assess, and address potential biases that may impact outcomes. Techniques for detecting and avoiding biases include data collection, pre-processing, feature engineering, data split/selection, model training, and model validation. By implementing best practices and using relevant examples at each stage of the pipeline, machine learning practitioners can reduce bias in their models and ensure more accurate predictions.