Algorithmic Fairness Testing for Inclusive Growth: Bridging Theory and Industrial Practice

The rapid growth of Artificial Intelligence systems across industries necessitates robust processes to ensure their ethical and fair use. A critical aspect of AI development is algorithmic fairness, which addresses potential biases that can lead to discrimination. This study bridges the gap between theoretical concepts of fairness and their practical application in the industry. It examines fairness measures, bias detection, and mitigation throughout the AI lifecycle, including data collection, model training, and ongoing monitoring. Additionally, it assesses the challenges and opportunities in implementing fairness testing in industrial pipelines, considering factors like computational costs and regulatory compliance. By integrating scholarly research with practical implementation plans, this paper offers actionable insights for researchers, practitioners, and policymakers to create equitable AI systems. It positions algorithmic fairness testing as a means for global prosperity by mitigating algorithmic harms in critical sectors. The study promotes inclusive growth, public trust, and equitable access to AI-enabled services through governance-aware practices tailored for various organizational contexts.