Adversarial debiasing is a technique used in machine learning (ML) to reduce bias in predictive models. It is an important approach in the field of ethical AI that aims to ensure that ML systems operate fairly across different demographic groups.
The process of adversarial debiasing typically involves two main components:
- Predictive Model: This is the main model that makes predictions based on input data.
- Adversarial Model: This model is trained to predict the sensitive attribute (such as race, gender, or income) from the outcomes of the predictive model. The idea is to reduce the ability of the adversarial model to accurately predict the sensitive attribute. If successful, the process ensures that the predictive model's outputs are less influenced by that attribute.
The training process involves a min-max game where the predictive model aims to maximize its performance on the specificied task (for example, classification), while the adversarial model aims to minimize its prediction error on the sensitive attribute. By doing this, the predictive model learns to make fairer and more equitable decisions.
