Combining Human-in-the-Loop Systems and AI Fairness Toolkits to Reduce Age Bias in AI Job Hiring Algorithms

Abstract

As artificial intelligence (AI) systems become more sophisticated, they are increasingly integrated into high-stakes decision-making processes, such as hiring, fraud detection, loan approvals, and medical diagnoses. However, this growing reliance on AI raises concerns about the potential for these systems to perpetuate and amplify societal biases. Researchers have developed two main approaches to bias mitigation in AI to address this issue: human-in-the-loop (HITL) systems and AI fairness toolkits. HITL systems involve human reviewers actively participating in the AI decision-making process, while AI fairness toolkits are software tools that can identify and mitigate bias. HITL systems are particularly effective in addressing biases tied to specific domains, while AI fairness toolkits can be useful in identifying and addressing bias proactively. This paper examines different combinations of HITL systems and AI fairness toolkits, conducts an experiment to evaluate biases in hiring decisions using each, and provides recommendations for organizations considering implementing one or both approaches.

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