Hiring AI Turns Familiarity Into an Applicant Advantage

The institutional problem is not only biased output; it is the quiet conversion of visibility into qualification. Once a hiring system treats familiar language, familiar backgrounds, and familiar self-description as stronger signals, applicants who already match the model's training world start closer to the shortlist . Coverage of self-preferencing research adds the procedural layer: model-written applications can become better tuned to model screening than human-written ones are . The new hiring advantage belongs to candidates who can make themselves resemble the system's prior exposure…