Algorithmic Bias in Artificial Intelligence-Assisted Hiring and Legal Exposure Under Anti-Discrimination Frameworks

Abstract

AI-assisted recruiting systems revolutionize the process of hiring through the automation of applicant screening, ranking, and decision-making. Yet, their increasing usage leads to several important issues such as algorithmic bias, discrimination at work, and lack of transparency and accountability. This research seeks to identify the causes and effects of algorithmic bias in recruitment assisted by AI and analyze the risks that arise from this practice from the perspective of anti-discrimination law. It will be analyzed how biased data used for training, inequality, selection of features, and the way algorithms are designed may lead to discriminatory outcomes and unfair employment practices. The role of companies and software vendors in providing transparency and accountability in the hiring process using AI will be explored. Possible methods for detecting and reducing bias in AI-assisted recruiting systems are discussed. For this reason, a governance-based model is suggested here that will include data governance, algorithmic impact assessment, fairness monitoring, and accountable human decision-making. This approach will be concerned with balancing risks associated with discrimination with maintaining efficiency in the hiring process. The key focus in this research is on integrating technical measures, organizational policies, and legal compliance within the whole life cycle of the hiring process based on artificial intelligence. It should be mentioned that the study is quite important for the issue of responsible AI governance and its role in ensuring a fair recruitment process.