Fairness in Machine Learning (FAccT)

Fairness in Machine Learning (FAccT) involves designing algorithms that make decisions without unfairly favoring or discriminating against specific groups, based on attributes like race, gender, or age. It aims to ensure that the outcomes are equitable and just, promoting unbiased treatment across diverse populations. This field addresses challenges like recognizing bias in data, developing methods to mitigate it, and creating models that make fair decisions, thereby fostering trust and ethical use of AI technologies in areas such as hiring, lending, and healthcare.