Summary
In this chapter, we covered fairness constraints as applied to different ML tasks. We started with the classification task and saw how we can add a regularizer to the loss function to mitigate unfairness. The chapter also covered how we can modify the loss function (objective) and mitigate unfairness. After that, we worked on regression tasks. There, again, we saw how adding a regularizer term can ensure fair algorithms. We covered the penalty terms for both individual and group fairness. Then, we explored the term that can be added to cluster a task to make it fair. We also discussed reinforcement learning and saw how fairness constraints can be added to the regret function. The recommendation task was considered next, where we showed how adding fairness constraints in the form of upper and lower bounds can help in mitigating unfairness. We also discussed how the recommendation task is similar and different compared to the other tasks. Finally, we covered the challenges in...