This article outlines a framework for mitigating algorithmic bias and hallucinations in LLM-driven job matching systems to ensure compliance with the EU AI Act and DSA. It highlights the risks of LLMs inventing skills or reinforcing historical biases, classifying such systems as "High-Risk" under the EU AI Act. The proposed solution involves a four-layer architecture: Retrieval Augmented Generation (RAG) to ground LLMs in verified data, Guardrail Orchestration for validation, Adversarial Testing, and Human-in-the-Loop (HITL) validation. AI
IMPACT Provides a technical framework for building compliant and reliable LLM applications in regulated industries like recruitment.
RANK_REASON The article describes a technical framework and architecture for using LLMs in a specific application (job matching) while addressing compliance and ethical concerns, rather than announcing a new model or research breakthrough.
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