Westworld Dizisinde Makine Öğrenmesi: Cinsiyet Eşitsizliğini Yeniden Üreten Yapay Zekâ Yaratımlar

Abstract

The term machine learning refers to the automatic detection of meaningful patterns in the data and has become a common tool in almost every job that requires obtaining information from large data sets in recent years.This has ultimately brought us to be surrounded by technology based on machine learning.On the other hand, the artificial intelligence creations developed with machine learning and deep learning methods are fed with biased information with the help of the internet network and restructures gender inequality.Feeding the artificial intelligence creations developed through machine learning and deep learning methods with situated knowledge through the Internet network restructures gender inequality.A deep learning method that enables computers to learn from experience, to understand the world in terms of concepts hierarchy, and to define each concept in relation to simpler concepts, saves all the dependencies that artificial intelligences need to learn from human beings to guide them.However, artificial intelligence creations that can think, create and exist with the guidance of people have to be fed with biased information until they have autonomous thinking development structure.Epistemological point of view theory that feminist theory frequently benefits from is based on the view that all attempts to know are socially positioned.The proxy, which provides the circulation of information, plays an active role in creating what they know about sex, class, race, ethnicity, sexual and physical capacity and limiting what they can know.The impact of social position on epistemic content and capacity not only shapes our understanding of the world, but also how it is presented to us through experience.This research, in the example of the Westworld series, aims to examine the robots that people program according to gender norms to the service of humans and especially men, with a feminist epistemology perspective method.It also aims to demonstrate that artificial intelligence modeling in the series causes consolidation of prejudiced and biased knowledge on the world and re-structuring of gender inequality.