Description: Bias mitigation refers to the strategies and techniques used to reduce bias in artificial intelligence (AI) models and algorithms. This concept is crucial in the development of AI systems, especially in various technological applications, where user interaction is constant and direct. Bias can arise from various sources, such as unrepresentative training data, design decisions, and result interpretation. Bias mitigation aims to ensure that AI models are fair, equitable, and do not perpetuate stereotypes or discrimination. This involves implementing methods that assess and correct bias at the design, training, and evaluation stages of models. The relevance of this practice has increased over the past decade as AI has become integrated into everyday applications, from virtual assistants to recommendation systems. Ethics in AI is also intrinsically related to bias mitigation, as an ethical approach to technology development involves recognizing and addressing inequalities that may arise from algorithmic decisions. In summary, bias mitigation is an essential component for the responsible development of artificial intelligence, ensuring that systems are inclusive and representative of the diversity of society.
