Bias in AI and machine learning often stems from the data used to train the models. If the training data is not representative of the problem space or population, the model can develop biased predictions. Synthetic data can help mitigate such biases in several ways.
Firstly, synthetic data allows for controlled data generation. This means one can generate a dataset that accurately represents different classes, scenarios, or populations that might be underrepresented in the real data. For instance, if a certain demographic is underrepresented in the real data, more synthetic data representing that demographic can be generated to ensure a balanced dataset.
Secondly, synthetic data can be used to generate data for scenarios that are rare or hard to capture in the real world but are important for training the model. For example, in autonomous vehicle development, synthetic data can simulate rare but critical situations, such as certain types of accidents or extreme weather conditions. This ensures the model is trained on these scenarios and can handle them appropriately.
Moreover, synthetic data can be used to understand the impact of bias in the models. By generating synthetic data with known biases and feeding this data to the model, one can observe how these biases affect the model's performance. This can provide valuable insights into how the model might behave when exposed to biased real-world data and guide the development of strategies to mitigate these biases.
We have written an entire blog series on Fairness and Bias that can be found here