#resume

The bias against human-written resumes is particularly substantial, with self-preference bias ranging from 67% to 82% across major commercial and open-source models. To assess labor market impact, we simulate realistic hiring pipelines across 24 occupations. These simulations show that candidates using the same LLM as the evaluator are 23% to 60% more likely to be shortlisted than equally qualified applicants submitting human-written resumes, with the largest disadvantages observed in business-related fields such as sales and accounting. We further demonstrate that this bias can be reduced by more than 50% through simple interventions targeting LLMs' self-recognition capabilities. These findings highlight an emerging but previously overlooked risk in AI-assisted decision making and call for expanded frameworks of AI fairness that address not only demographic-based disparities, but also biases in AI-AI interactions.

Most job appli­ca­tions today are screened by AI-​driven appli­cant track­ing sys­tems (ATS) before a human ever sees them. That means for­mat­ting con­sis­ten­cy, key­word align­ment, and clar­i­ty aren’t just nice to have — they’re sur­vival traits. Manually tai­lor­ing each ver­sion is slow and error-prone.

"The terrifying reality regarding your resume is that for all the many hours you put into fine-tuning, you've got 30 seconds to make an impression on me. Maybe less."

"Yes, the irony of a guy who once worked for the premiere wiki software company just now realizing the benefits of using a wiki in this case is, well… let’s not go any further. I get it, OK?"