Cracking the AI recruitment code: Striving for transparency in finding the right person–job fit

The use of artificial intelligence (AI) has significantly enhanced the efficiency of resume screening; however, discrepancies in person–job fit assessments between AI and human evaluators can adversely affect the recruitment process. This study introduces the concept of "person–job fit perception difference" to describe these discrepancies and proposes a theoretical model outlining the relationships among person–job fit perception difference, AI transparency, and algorithmic literacy. Based on data from a 2 × 3 factorial-design experiment (N = 286), the findings reveal that both external transparency and functional transparency of AI recruitment systems negatively influence the person–job fit perception difference. Additionally, two distinct aspects of algorithmic literacy moderate different pathways in this process.