Creating a product to segment donors and predict donor churn - ai ethics in NGO-s: implications of biased or unfair machine learning in the non-profit sector

Han, QiweiBilro, Ana Raquel de Oliveira2022-07-292022-07-292022-01-202021-12-17http://hdl.handle.net/10362/142645The social sector is far from thriving nevertheless with the help of emerging computation technologies advancements can be done, compensating the scarcity of resources. With these systems, organizations can classify their donors and target those who prosper more return. Nonetheless, the use of these models should consider bias and fairness as central questions. In a sector focused on social good, the ethical challenges of machine learning are key. This thesis focused on a case study, done in Portugal 2021, but aims to take the conversation on bias and fairness further, addressing the technological challenges and limitations of machine learning tools.engFairnessMachine learningBusiness analyticsBusiness and data analyticsNon-profit organizationsBiasCreating a product to segment donors and predict donor churn - ai ethics in NGO-s: implications of biased or unfair machine learning in the non-profit sectormaster thesis203039874