ARTIFICIAL INTELLIGENCE AND ALGORITHMIC DISCRIMINATION:

AN ANALYSIS OF THE AMAZON CASE

Abstract

This article analyzes the issue of algorithmic discrimination in the context of artificial intelligence (AI), focusing on a case involving Amazon. It highlights how AI algorithms can inadvertently perpetuate biases and prejudices, affecting minority groups in society. The study addresses how Amazon's hiring algorithm favored male candidates over female candidates. The analysis underscores the importance of training and adjusting AI algorithms to prevent discrimination and bias, emphasizing the need for greater human oversight and regulation to ensure equity in the implementation of AI in business environments. Scientifically, the study used the literature review method, consulting national and international doctrine and jurisprudence to understand the ethical challenges of AI. The results revealed that algorithmic discrimination is significantly influenced by the quality of training data and the assumptions embedded in the algorithms. A notable example is the Amazon case, where a resume screening system was trained with data reflecting the male predominance in the tech industry, resulting in a bias against female candidates.