Fairness and Transparency in AI Models focus on ensuring that machine learning systems make unbiased, explainable, and accountable decisions. Fairness in AI addresses issues such as bias detection, data imbalance, and equitable treatment across different groups. Transparency ensures that model decisions can be understood and interpreted by humans through explainability techniques. This training explains how biases can emerge in data and algorithms and how to mitigate them using fairness metrics and model evaluation methods. It also covers explainable AI techniques such as feature importance, SHAP values, and model interpretability tools. You will learn how to design responsible AI systems that promote trust, accountability, and ethical decision-making. The course also highlights best practices for building fair, transparent, and compliant AI solutions.
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