The growing use of AI systems has intensified discussions around fairness and bias in artificial intelligence as potential biases and discrimination become more apparent. This study examines the sources, impacts, and mitigation strategies related to fairness and bias in AI. Multiple studies have identified biases against certain groups in AI systems, such as the facial recognition systems studied by Buolamwini and Gebru [1] and hiring algorithms examined by Dastin [2]. These biases can perpetuate systemic discrimination and inequality, with detrimental effects on individuals and communities in areas like hiring, lending, and criminal justice. 1. Buolamwini, J.; Gebru, T. Gender shades: Intersectional accuracy disparities in commercial gender classification. In Proceedings of the 1st Conference on Fairness, Accountability and Transparency, New York, NY, USA, 23–24 February 2018; pp. 77–91. 2. Dastin, J. Amazon scraps secret AI recruiting tool that showed bias against women. In Ethics of Data and Analytics; Auerbach Publications: Boca Raton, FL, USA, 2018; pp. 296–299. FERRARA, Emilio. Fairness and Bias in Artificial Intelligence: A Brief Survey of Sources, Impacts, and Mitigation Strategies. Sci 2024, 6, 3. Disponível em: https://doi.org/10.3390/sci6010003. Acesso em: 12 ago. 2025. Adaptado.