Implementing Bias Mitigation Techniques
Practical techniques for bias mitigation:
Diverse Data Collection: Assembling wide-ranging datasets is crucial for building inclusive AI systems.
Bias Detection and Regular Audits: Implementing processes to identify and examine biases within AI systems.
Bias Mitigation Algorithms: Applying specific algorithms to reduce bias in traditional AI products.
Evaluation Metrics for Language Models: Using metrics like recall and precision to assess and mitigate bias in language generation and retrieval processes.