Banca de DEFESA: LUANY AZZAM VLIESE

Uma banca de DEFESA de MESTRADO foi cadastrada pelo programa.
STUDENT : LUANY AZZAM VLIESE
DATE: 06/02/2026
TIME: 10:00
LOCAL: google meet
TITLE:

Artificial Intelligence and algorithmic discrimination in automated hiring

KEY WORDS:

Algorithmic discrimination. Artificial Intelligence. Labor Law. Gender equality. Algorithmic governance.

PAGES: 90
BIG AREA: Outra
AREA: Multidisciplinar
SUMMARY:

This dissertation analyzes the impacts of algorithmic discrimination in automated hiring processes, particularly within labor relations, through an interdisciplinary approach that encompasses legal, technical, and ethical aspects. It begins with the recognition that Artificial Intelligence (AI) and Machine Learning (ML) systems are increasingly used in recruitment processes, offering promises of efficiency and impartiality. However, far from being neutral, these systems can reproduce and even intensify historical inequalities, disproportionately affecting vulnerable groups based on gender, race, age, and social class. The methodology involves bibliographic and documental research, with critical analysis of national and international legislation, and specialized literature in the fields of Law, Computer Science, and Ethics. In the legal domain, the study examines the normative framework that guarantees equality and non-discrimination in labor relations, highlighting the constitutional principles of human dignity, substantive equality, and the prohibition of discrimination. It also connects the discussion to the United Nations 2030 Agenda for Sustainable Development, particularly SDGs 5, 8, and 10. From a technical perspective, the study explores the foundations of algorithms used in recruitment, types of machine learning, and the critical role of training data, illustrating how biases can be introduced at every stage of system development. Practical examples of algorithmic discrimination are presented, along with the risks posed by opaque systems ("black boxes") and their social effects on vulnerable populations. The findings indicate an urgent need for specific regulation to ensure the ethical and responsible use of AI in recruitment. The dissertation evaluates strategies for bias mitigation, labor compliance practices, algorithmic auditing, and explainability techniques, showing that technological fairness requires proactive governance aligned with fundamental rights. It concludes that algorithmic discrimination is not an inevitable side effect of technology, but rather a consequence of human decisions that can, and must, be regulated, audited, and corrected. The study proposes pathways to build fairer and more transparent recruitment systems, consistent with constitutional principles and international commitments to equality.

COMMITTEE MEMBERS:
Externa à Instituição - GIULIA PAROLA
Interno - 1980786 - MARCEL WILLIAM ROCHA DA SILVA
Presidente - 1692240 - RODRIGO DE SOUZA TAVARES