Mitigating AI bias is a critical concern to ensure fairness and trust in AI systems. As of December 2024, several strategies and methods have been developed to address and reduce bias in AI. Here are some key approaches:
1. Data Preprocessing Techniques
Normalization and Standardization: Ensuring that data is consistent and free from identifiable information that could introduce bias.
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Data Augmentation: Creating synthetic data to balance underrepresented groups in the dataset.
Transparency and Explainability: Providing insights into the key features and decision-making processes of AI models to understand how and why certain decisions are made, which can be crucial for detecting biases.