Bias and fairness in explainable Artificial Intelligence systems

This nugget discusses the critical issues of bias and fairness in AI systems, using a practical application in steel plate production to illustrate the implications. It emphasizes that AI systems can inherit biases from their training data, which can lead to unfair treatment of suppliers and affect product quality. The importance of fairness in AI is highlighted, not only as an ethical concern but also as an operational and economic imperative in manufacturing. The segment explores techniques for overcoming bias and ensuring fairness, including data preprocessing, model regularization, explainability tools, and continuous monitoring, with the goal of promoting equitable treatment of suppliers, enhancing product quality, and strengthening trust in the AI system.