Systematic Literature Review: Fair By Design

Abstract

The rapid adoption of Artificial Intelligence has raised concerns about potential bias,discrimination, and ethical use of social markers. This systematic literature review(SLR) examines the principles and guidelines for distinguishing between fair and unfair discrimination in AI systems, focusing on their use of social markers such as race, gender,age, disability, socioeconomic status, intersectionality, and many more. Guided by research questions, the review investigates the conditions that justify the inclusion of social markers, the implementation of these distinctions in recruitment frameworks, andthe current practices addressing fairness and bias in AI systems. A comprehensive searchand selection process included 91 articles in English and publications from 2018 to 2024.These sources were analyzed to uncover themes related to data bias, proxy discrimination,intersectionality, explainability, and the role of regulatory frameworks. Our analysis reveals that the ethical use of social markers is contingent on transparent, fairness-driven applications designed to mitigate systemic inequities and improve inclusivity. However,our study also points out major hazards, including opaque decision-making procedures,inadequate responsibility, and growing historical prejudices. This study emphasizes the need to include substantial fairness criteria, governance structures, and stakeholderviewpoints in artificial intelligence evolution. This research contributes to the fieldby providing actionable insights into designing AI systems that align with ethical and current legal standards for fairness. It highlights the need for intersectional approaches and continuous auditing to address the complexities of fairness and discrimination in automated decision-making, particularly in AI recruitment contexts. The findings serveas a foundation for future research and development in responsible AI.