Description: Bias in image recognition refers to the tendency of artificial intelligence (AI) systems to produce unequal or unfair results based on characteristics such as race, gender, or age of the individuals in the analyzed images. This phenomenon can arise due to the quality and diversity of the data used to train AI models. When datasets are limited or do not adequately represent all populations, algorithms may learn patterns that perpetuate stereotypes or discrimination. Bias in image recognition not only affects the accuracy of results but also raises serious ethical concerns, as it can influence critical decisions in areas such as public safety, hiring practices, and access to services. The lack of transparency in how these systems are trained and evaluated exacerbates the problem, making it difficult to identify and correct biases. Therefore, it is essential to address this issue to ensure that image recognition technology is used fairly and equitably, promoting inclusion and diversity in the development of AI solutions.
