In this paper, we introduce a novel postprocessing algorithm that is both model-agnostic Although rarely discussed, expecting the debiasing method and does not require the sensitive attribute at test to perform a low number of prediction changes is especially time. In addition, our algorithm is explicitly designed interesting in contexts where fairness is enforced while a to enforce minimal changes between biased model is already in production (Krco et al., 2023). In realworld and debiased predictions--a property that, applications, maintaining the integrity and reliability while highly desirable, is rarely prioritized as an of predictive models is crucial, especially when they have explicit objective in fairness literature. Our approach undergone rigorous validation and expert review. For example, leverages a multiplicative factor applied in non-life insurance pricing, experts commonly to the logit value of probability scores produced employ Generalized Additive Models (GAMs) with splines by a black-box classifier. We demonstrate the efficacy or polynomial regression on Generalized Linear Models to of our method through empirical evaluations, ensure that price are justifiable and align with both business comparing its performance against other four debiasing objectives and customer expectations (e.g., avoiding price algorithms on two widely used datasets in increases that could negatively impact customer satisfaction fairness research.