Reimagining Diversity And Inclusion In HR Practices With AI-Driven Fairness Algorithms For Bias Mitigation And Equity Optimization

Abstract

The awareness of workforce diversity and diversity in the workplace has put into sharp focus equal opportunity Human Resource (HR) practices. Many of the traditional practices employed in the field of HR are not immune to bias and organizational discrimination and thus cannot guarantee fairness in the recruitment and selection processes. There has been multiple addressed attempts to mitigate the problem of bias which mostly involve the use of manual supervision or rules which do not pose scalability, adjustability and do not perform well in complex organizations. The objectives of this study will be to deploy and operationalise fairness algorithms using AI to enhance fairness within Human Resource systems. The overall goal is to develop an approach based on data to identify and address potential bias in both algorithm and human-related processes in HR. The contribution of this research is threefold, in its proposal of fairness-aware machine learning models, its incorporation of ethical governance, and its flagging of bias and decision-making. Not only do some of the features of the proposed system allow for the misleading biases to be avoided when it comes to candidates’ evaluation but also interpretability and auditability features. Advantages show a notable decrease of the biased decision rate and an increase of the fairness ratio on the examined datasets of recruitment. The study further notes that ‘the AI cannot do the human judgment but it can do so when used with ethical and strategic purpose’. From the above findings, it can be concluded that integrating AI with DEI is the perfect way to work towards a better future for everyone.