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Advancing recruitment: fair and efficient resume screening with LLMs
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Orientador(es)
Resumo(s)
Traditional talent acquisition encounters significant sustainability problems marked by increasing application quantities and the intrinsic dangers of subjective human bias. This article, which is still being worked on, uses the Design Science Research (DSR) technique to create CurAIEval, a new IT tool for automated, fairness-aware resume screening. Based on a rigorous examination of the literature, the study finds a fundamental gap between technical optimization and ethical governance. To close this gap, the suggested architecture combines Large Language Models (LLMs) for semantic analysis with a required "Fairness-by-Design" auditing engine. The system uses a Human-in-the-Loop (HITL) protocol to automate parsing of large amounts of data while still letting people make the ultimate judgments. Expected outcomes encompass the creation
of a workable prototype that substantially alleviates administrative burdens and the validation of a Balanced KPI Framework. This approach is meant to quantify both operational efficiency (like time-to-hire) and algorithmic fairness (like disparate impact) at the same time. It gives a validated technique to automate hiring in a responsible and fair way.
Descrição
Palavras-chave
LLM-Based Recruitment Algorithmic Fairness Resume Classification Design Science Research
Contexto Educativo
Citação
Novais, L., Rocio, V., Oliveira, P. & Santos, A. (2025, dezembro 9). Advancing recruitment: fair and efficient resume screening with LLMs. In Sá, C., Oliveira, C., Silva, E., Cardoso, M., Morgado, N., Proença, P., Carvalho, P., Vieira, R., Meireles, R., & Moreira, S. (Eds.). Simpósio de Engenharia Informática 2025. Instituto Superior de Engenharia do Porto ISEP – P.Porto
Editora
Instituto Superior de Engenharia do Porto (ISEP) – P.Porto