Machine Learning Bias and Fairness

Machine learning is increasingly integral to decision-making processes across various sectors, yet it faces challenges of bias which can result from non-representative training data, historical societal biases, and human errors during data preparation. Bias in machine learning models can lead to unfair decisions, particularly impacting specific groups, and is a significant concern in regulated industries where decisions may be audited. Addressing this issue involves actively monitoring models for biased outputs, retraining with more representative data, and adjusting model parameters to ensure fairness. Tools like Seldon provide solutions for real-time machine learning deployment, offering standardization, observability, and scalability to help organizations manage and mitigate bias effectively while maximizing efficiency and innovation.