AI Self-preferencing in Algorithmic Hiring: Empirical Evidence and Insights

Original Article ↗ Hacker News Discussion ↗

Large language models used in resume screening appear to favor resumes written in their own style, potentially giving applicants who use the same tools to generate or polish their CVs an advantage. Commenters describe an emerging feedback loop where employers automate filtering with AI, candidates optimize resumes for those systems, and documents become less meaningful for human readers, with some fearing “slop all the way down” and degraded hiring quality. Others question the underlying research methods and note legal, ethical, and practical concerns about opaque automated decision‑making in recruitment.

Observed Self-Preferencing Behavior

  • Commenters generally find it intuitive that LLMs prefer resumes they (or their own family) generated.
  • Explanation: models generate text aligned with their internal “corporate-speak” heuristics, then rate that same style as higher quality when screening.
  • Similar behavior noted in other contexts: models prefer their own plans/designs and may overrate their own outputs vs human ones.

Implications for Candidates (“Resume SEO”)

  • Many argue that if employers use LLMs/ATS with AI layers, not using an LLM to “optimize” your resume is now playing on “hard mode.”
  • Some suggest using the same LLM as the employer’s stack (if known) to gain an advantage.
  • Others joke about multi-LLM “arms races”: applying multiple times with different LLM-crafted resumes, or submitting separate “for-AI” and “for-human” versions.

Anecdotes and Practical Outcomes

  • Multiple posters report substantially better response rates after letting an LLM rewrite or heavily polish resumes/LinkedIn profiles, despite skepticism about the style.
  • A few hiring managers say they can often recognize AI-written resumes and view them negatively; others accept them as necessary in an AI-filtered pipeline.
  • Some recruiters/hiring managers claim to still do mostly human review (often after keyword-based pre-sorting); others describe overwhelming volumes that make some automation inevitable.

Bias, Quality, and Ethical Concerns

  • Worry that AI filters will favor AI-“sanitized” language over authentic human writing, pushing everyone toward bland, homogeneous resumes.
  • Concern that models lack nuance, reward verbosity and repetition, and may hallucinate achievements, degrees, and metrics.
  • Several fear a feedback loop: LLMs trained on LLM-generated content, deepening biases and “enshittifying” not just products but hiring norms.
  • Some note GDPR/automated-decision rules could, in theory, be invoked against fully automated rejection, but enforcement is seen as doubtful.

Skepticism About the Research & Alternatives

  • One detailed critique says the study design (rewriting only executive summaries and rating them in isolation) may exaggerate effects and not reflect real hiring.
  • Others argue the whole setup is contrived: comparing two versions of the same resume doesn’t show real-world mis-selection.
  • Proposed alternatives:
    • Use LLMs only as feature extractors and train simple, transparent models on top.
    • Rely more on code review or work samples, or standardized tests / lotteries, instead of resumes.