Blinded by Context: Unveiling the Halo Effect of MLLM in AI Hiring

Kyusik Kim, Jeongwoo Ryu, Hyeonseok Jeon, Bongwon Suh

Abstract: This study investigates the halo effect in AI-driven hiring evaluations using Large Language Models (LLMs) and Multimodal Large Language Models (MLLMs). Through experiments with hypothetical job applications, we examined how these models’ evaluations are influenced by non-job-related information, including extracurricular activities and social media images. By analyzing models’ responses to Likert-scale questions across different competency dimensions, we found that AI models exhibit significant halo effects, particularly in image-based evaluations, while text-based assessments showed more resistance to bias. The findings demonstrate that supplementary multimodal information can substantially influence AI hiring decisions, highlighting potential risks in AI-based recruitment systems.

External IDs:dblp:conf/acl/KimRJS25