From the course: Building AI-Ready Applications with Azure Databases and AI
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AI fairness: Bias mitigation techniques - Azure Tutorial
From the course: Building AI-Ready Applications with Azure Databases and AI
AI fairness: Bias mitigation techniques
- [Instructor] Let's discuss the importance of ethical AI practices and explore techniques for ensuring fairness and responsibility in AI development. Fairness can be implemented as a critical data pre-processing method, like resampling and reweighting. AI models can propagate biases present in the training data, leading to unfair outcomes. Ensuring fairness in AI involves identifying and mitigating these biases to create equitable and just systems. There are several techniques to mitigate bias in AI models. These include post-processing techniques like adjusting the CN thresholds. By applying these techniques, you can reduce bias and improve the fairness of your AI model.
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