Uma revisão de escopo sobre importância da neutralidade de viés e transparência dos dados em sistemas de recomendações

Abstract

Recommender systems play a crucial role in personalizing experiences on digital platforms, influencing activities ranging from online shopping to content consumption. Despite their benefits, such as efficiency and convenience, these systems face criticism for biases, lack of transparency, and negative social impacts, raising ethical concerns regarding fairness and reliability. We conducted a scoping review to analyze how these issues have been addressed in the literature, with a focus on bias neutrality and data transparency. This review identifies 14 relevant studies and highlights that the primary types of bias include popularity, selection, and homogenization, while the lack of transparency undermines user trust and limits recommendation diversity. The findings indicate that mitigating these issues requires strategies such as diversifying recommendations, providing clear explanations to users, and implementing techniques to regulate biases. Furthermore, they emphasize challenges such as algorithmic opacity and the need for adaptable regulation. We conclude that developing ethical recommender systems requires integrating neutrality, transparency, and social responsibility, paving the way for algorithmic improvements and more inclusive policies.