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    Discriminação algorítmica no mercado de trabalho brasileiro: uma análise da utilização da IA
    (Centro Universitário do Estado do Pará, 2024-09-07) Brasil, Luiza Arruda Câmara; Ferreira, Vanessa Rocha; http://lattes.cnpq.br/8565252837284537; Soares, Dennis Verbicaro; http://lattes.cnpq.br/9961080231553419; Resque, João Daniel Daibes; http://lattes.cnpq.br/0225226226260524
    This discussion is aimed at investigating the general nature of the main algorithmic discriminations found in the Brazilian travel market. The Brazilian trade market has now been strongly impacted by artificial intelligence, revealing significant challenges to combat algorithmic discrimination, just as it currently does on data protection (Lei 13,709/2018). The research focuses on investigating which are the main forms of algorithmic discrimination and how they can be mitigated, through an analysis of the current Brazilian scenario in traffic and data protection norms. The specific objectives include analyzing the historical and conceptual aspects of AI, in order to understand how this technology evolved and so that it can be understood the ambiguities created in its use; Examine the trade market and how the algorithms impact the trade-home relationship, through the programs provided. Understand how the European Union (EU) deals with algorithmic discrimination and understand the consequences of varying the minimum parameters for use by AI in Brazil, resulting in algorithmic discrimination in the trade market. The theoretical relevance of the research is based on the production of content that can contribute to the regulatory debate on a technology that impacts millions of human beings, with the main purpose of protecting the fundamental and social rights of Brazilian workers. You do not intend to comply with the use of this technology, but only meet the minimum requirements for its use. From a practical point of view, in this article you can contribute as a debate, in order to propose a new approach to the consequences of the indiscriminate use of artificial intelligence. Having said that, using the hypothetical-deductive method. With a qualitative approach, an appropriate analysis of bibliographic data, documentary analysis and division into chapters that are explored or used by artificial intelligence in the business market. Finally, the considerations stand out as to the importance of testing against persistent problems, trying to combat algorithmic opacity.