Humanizing ATS-Based Recruitment Using LLMs and Human-in-the-Loop Oversight

Application Tracking Systems (ATSs) have evolved significantly since their inception in 1996, transitioning from simple resumérepositories to AI-driven tools with advanced capabilities. While these developments have improved recruitment efficiency, they have also raised important ethical, organizational, and human-rights-related concerns. Bias in machine learning (ML) training data, opaque decision criteria, and excessive reliance on automated judgment may contribute to unfair treatment, reduced transparency, and limited human oversight in hiring processes. This study addresses these challenges by proposing a human-centered approach to ATS-supported recruitment based on a set of Humanization Services. Using a Design Science Research approach, three main artifacts were developed: a Job Requirements Validation Module, a Bias Trigger Removal Module, and a blockchain-supported dual-authorization mechanism for vacancy approval, which requires digital signatures from qualified professionals to approve job postings, ensuring that there are humans that assume responsibility. These components are intended to improve job posting quality, reduce bias-conducive information in applicant data, and strengthen accountability in recruitment workflows. The evaluation provides initial empirical support for the operational feasibility of the proposed approach under the tested conditions. The study therefore contributes a practical and theoretically grounded step toward more transparent, accountable, and human-centered AI-supported recruitment.