Mitigating Algorithmic Bias in AI-Driven Hiring Systems in the United States

Abstract

The purpose of the research is to familiarise itself with the problems that shape the mitigation of algorithm bias in hiring systems founded on AI in the United States. Particularly, it investigates the contribution of awareness, bias belief, mitigation, organisational practices and implementation issues to the judgement of the trust and fairness of the AI-based recruitment process. The study presents the quantitative method of research and conducts a survey interviewing 300 HR professionals, recruiters, data scientists and managers. This questionnaire included 26 questions and a five-point Likert-type scale. Data analysis was conducted with the help of descriptive analysis, reliability tests, correlation, regression, and ANOVA to compare and investigate associations between variables.

The results indicate that awareness and perception of algorithm bias are high in the sample. The strongest predictor of trust and results was mitigation strategies followed by practices. Correlation analysis revealed that there was positive relationship between awareness, mitigation strategies and trust and negative relationship between challenges and all the key variables. The regression analysis demonstrated that the 61% of the variance in outcomes and trust was explained by the framework.

The study will provide effective recommendations to companies interested in implementing ethical AI-based recruitment technologies. It points to the necessity to introduce effective methods of bias reduction, enhance governance, and train employees. It further emphasizes the need to overcome implementation obstacles, including technical limitations and resource distributions, to promote fairness and transparency. This study adds to the AI ethics research literature by providing empirical evidence on bias minimization in employment algorithms. It combines both technical and organisational and perceptual variables into a single framework and offers insights into the manner through which trust and fairness can be achieved in AI-powered hiring systems.

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