Mitigating algorithmic bias

Even after understanding and measuring bias in ML, the job is only half done. The next logical step is to implement strategies for mitigating bias. Various techniques exist, each with its strengths and weaknesses, and a combination of these strategies can often yield the best results. Here are some of the most effective methods:

• Preprocessing techniques: These techniques involve modifying the data before inputting it into the ML model. They could include techniques such as resampling to correct imbalances in the data, or reweighing instances in the data to reduce bias.
• In-processing techniques: These are techniques that modify the model itself during training to reduce bias. They could involve regularization techniques, cost-sensitive learning, or other forms of algorithmic tweaks to minimize bias.
• Postprocessing techniques: These techniques are applied after the model has been trained. They can include modifying the outputs based on the...