Fairness in Risk Estimation of Brazilian Public Contracts - de Lima, Órion Darshan Winter; Andrade, Nazareno (Anais do Symposium on Knowledge Discovery, Mining and Learning (KDMiLe), 2019)
Brazilian government agencies are currently using machine learning models to make public contracts audition through risk estimation. Recent works have shown that decision making models, like risk estimation, may be unfair. Despite the fact that risk estimations of public contracts may be unfair, no studies evaluating model fairness have been found. This work contributes by analysing fairness over risk estimation of brazilian public contract. This article found that currently used models are unfair and biased towards a specific class. This means that people within this class may be negatively affected by these decision making models unfairness through risk estimation of their companies.