Algorithmic Discrimination
Ethics & Safety
Techniques during data collection, training, or post-processing to reduce unfair biases in AI models.
Detailed Explanation
Algorithmic Bias Mitigation involves implementing strategies at various stages—such as data collection, model training, and post-processing—to identify and reduce biases that may lead to unfair or discriminatory outcomes in AI systems. These techniques ensure the model's predictions are more equitable across different demographic groups, promoting fairness, accountability, and ethical use of AI technology.
Use Cases
- •Ensuring fair loan approvals across demographics by applying bias mitigation techniques during model development and deployment.
Related Terms
Other terms in the Ethics & Safety category