ALGORITHMIC DECISION MAKING: ETHICAL AND MANAGERIAL CHALLENGES IN THE AGE OF ARTIFICIAL INTELLIGENCE

Abstract

The growing diffusion of artificial intelligence systems in organizational and institutional decision-making processes brings into focus deep tensions between technological efficiency and the protection of fundamental rights. This study analyzes the ethical and managerial challenges associated with algorithmic decision-making in the Brazilian context, justified by the gap identified in the national literature regarding the articulation between accountability, algorithmic bias, and organizational governance. The methodology adopts an exploratory bibliographic approach, supported by a systematic narrative review of the literature and documentary analysis of Brazilian peer-reviewed scientific periodicals, covering publications from 2020 to 2026. The results indicate that the main challenges are organized around three interconnected axes: the opacity of algorithmic systems, the reproduction of structural discrimination, and the lack of clear attribution of responsibilities between organizations and automated systems. The study reveals that the Brazilian regulatory framework — centered on the General Data Protection Law (LGPD) and the pending Artificial Intelligence Bill 2338/2023 — presents notable limitations in addressing the full range of ethical risks generated by automated decision systems. It is concluded that effective artificial intelligence governance requires integrated models that articulate transparency, accountability, and human oversight, overcoming both the limitations of current regulatory instruments and the cultural resistances embedded in organizational practices. The study contributes to the advancement of the field by consolidating a critical and interdisciplinary analysis of this phenomenon in Brazil, offering a solid foundation for future empirical investigations and for the formulation of more comprehensive public policies on algorithmic governance.