  Resilience to class imbalance and confounding biases, together with the
assurance of fairness guarantees are highly desirable properties of autonomous
decision-making systems with real-life impact. Many different targeted
solutions have been proposed to address separately these three problems,
however a unifying perspective seems to be missing. With this work, we provide
a general formalization, showing that they are different expressions of
unbalance. Following this intuition, we formulate a unified loss correction to
address issues related to Fairness, Biases and Imbalances (FBI-loss). The
correction capabilities of the proposed approach are assessed on three
real-world benchmarks, each associated to one of the issues under
consideration, and on a family of synthetic data in order to better investigate
the effectiveness of our loss on tasks with different complexities. The
empirical results highlight that the flexible formulation of the FBI-loss leads
also to competitive performances with respect to literature solutions
specialised for the single problems.
