Description: The ‘Black Box’ refers to a system whose internal mechanisms are not visible or understandable, making it difficult to interpret its decisions. This concept is particularly relevant in the field of artificial intelligence (AI), where complex algorithms, such as deep neural networks, can produce accurate results without users understanding how they were achieved. The lack of transparency in these systems raises important ethical questions, as decisions made by AI can significantly impact people’s lives, from candidate selection in hiring processes to sentencing determinations in the judicial system. The ‘Black Box’ becomes a challenge for accountability, as it is difficult to identify biases or errors in the decision-making process. Furthermore, the opacity of these systems can lead to distrust among users, who may question the legitimacy of automated decisions. Therefore, the discussion about the ‘Black Box’ in AI not only focuses on the technology itself but also on the ethical and social implications that arise from its use, highlighting the need to develop methods that increase the transparency and interpretability of these systems.
