Algorithmic Bias and Data Justice: ethical challenges in Artificial Intelligence Systems
Year: 2025
Type: article
Abstract: This article examines the critical ethical challenges posed by algorithmic bias in artificial intelligence (AI) systems, focusing on its implications for social justice and data equity. Through a systematic review of case studies and theoretical frameworks, we analyze how biased datasets and algorithmic designs perpetuate structural inequalities, particularly affecting marginalized communities. The study highlights key examples, such as gender and racial biases in facial recognition and hiring algorithms, while exploring mitigation strategies rooted in data justice principles. Additionally, we evaluate regulatory responses, including the European Union's AI Act, which proposes a risk-based governance framework. The findings underscore the urgent need for interdisciplinary approaches to develop fairer AI systems that align with ethical standards and human rights.
Source: EthAIca
DOI: 10.56294/ai2025159
Authors: Javier Gonzalez-Argote, Emanuel Maldonado, Karina Maldonado
Institution: AG Editor (Uruguay)
Language: English
Subfield: Safety Research
Field: Social Sciences
Domain: Social Sciences
Open Access status: hybrid
OpenAlex ID: W4411475682