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.

翻译：对阶级不平衡和分化偏见的适应能力和对公平保障的保证是具有实际影响的自主决策系统非常可取的特性。提出了许多不同的有针对性的解决办法,以分别解决这三个问题,尽管似乎缺乏统一的观点。我们通过这项工作提供了一种一般性的正规化,表明它们是不平衡的不同表现。根据这种直觉,我们制定了统一的损失纠正办法,以解决公平、比泽斯和平衡(FBI-loss)问题。拟议办法的纠正能力根据三个现实世界基准进行评估,每个基准都与审议中的问题之一有关,并按合成数据组来评估,以便更好地调查我们在不同复杂任务上损失的有效性。经验结果突出表明,灵活地拟订联邦调查局损失也导致在单一问题专门解决文献方面有竞争性的表现。