| dc.description.abstract | Gender bias in artificial intelligence (AI) systems, particularly within education and workplace settings,
poses serious ethical and operational concerns. These biases often stem from historically skewed datasets and flawed
algorithmic logic, which can lead to the reinforcement of existing inequalities and the systematic exclusion of
underrepresented groups, especially women. This systematic review analyses peer-reviewed literature from 2010 to 2024,
sourced from IEEE Xplore, Google Scholar, PubMed, and SpringerLink. Using targeted keywords such as AI gender bias,
algorithmic fairness, and bias mitigation, the review assesses empirical and theoretical studies that examine the causes of
gender bias, its manifestations in AI-driven decision-making systems, and proposed strategies for detection and mitigation.
Findings reveal that biased training data, algorithm design flaws, and unacknowledged developer assumptions are primary
sources of gender discrimination in AI systems. In education, these systems affect grading accuracy and learning outcomes;
in workplaces, they influence hiring, evaluations, and promotions. Mitigation approaches can be categorized into three
main categories: data-centric (e.g., data augmentation and data balancing), algorithm-centric (e.g., fairness-aware learning
and adversarial training), and post-processing techniques (e.g., output calibration). However, each approach faces
implementation challenges, including trade-offs between fairness and accuracy, lack of transparency, and the absence of
intersectional bias detection. The review concludes that gender fairness in AI requires integrated strategies that combine
technical solutions with ethical governance. Ethical AI deployment must be grounded in inclusive data practices,
transparent protocols, and interdisciplinary collaboration. Policymakers and organizations must strengthen accountability
frameworks, such as the EU AI Act and the U.S. AI Bill of Rights, to ensure that AI technologies support equitable
outcomes in education and employment. | en_US |
