Özet:

The term machine learning means the automatic detection of meaningful patterns in data and has become a common tool in almost every business that requires information from large data sets in recent years. As a result, this situation has brought us to be surrounded by a machine-based technology. On the other hand, the feeding of artificial intelligence creations developed by machine learning and deep learning methods, by the help of the internet network, with side-in-depth inserted information, restructs gender inequality in society. The deep learning method that allows computers to learn from their experiences, to understand the world in terms of the hierarchy of concepts, and to define each concept with its relationships with simpler concepts, saves from all the dependencies that they need to guide people in advance, because artificial intelligence acquires knowledge from the experience. However, the creations of artificial intelligence that can think, create and exist by human guidelines must be fed by side knowledge until they have an autonomous thinking development structure. The viewpoint epistemology, which is often used by the feminist theory, is based on the view that all knowledge efforts are socially positioned. The gender, class, race, ethnic origin and sexual and physical capacity of the agent that facilitates the circulation of information plays an effective role in creating and limiting what they know. The impact of social position on epistemic content and capacity not only shapes our way of understanding the world but also how it is presented to us through experience. The study aims to examine the status of robots programmed by people according to the social gender standards in the Westworld series, and in particular men, by the feminist epistemology point of view, to show that the artificial intelligence modeling in the series has led to the consolidation of the pre-judicial and side knowledge that exists in the world, and the re-establishment of social gender inequality.

Anahtar Kelimeler: