Summary
This chapter provided an extensive exploration of the pervasive challenge of bias in machine learning. It started by explaining various forms of bias inherent in machine learning models and examined their impact on different industries. The emphasis was on recognizing, monitoring, and mitigating bias, underscoring the importance of collecting data with minimal selection and sampling bias.
The central theme advocated a data-centric imperative over a model-centric one in addressing bias. Techniques such as oversampling, undersampling, feature selection enhancement, and anomaly detection were explored for bias rectification. Shapley values play a crucial role in bias identification, emphasizing the removal of examples with misaligned high Shapley values and the reintroduction of data points with replacement to improve ratios. Stratification of misclassified examples based on sensitive variables such as SEX was outlined for targeted bias correction.
The chapter concluded...
