AI-generated cover letters are now standard. HR teams are asking whether to screen for them. The honest answer is: it depends on what you are actually trying to find out, and whether you can act on the result responsibly.
In 2026, the majority of cover letters submitted to competitive job postings show some AI involvement. A 2025 survey by Resume Genius found that 72% of job seekers had used AI tools to write or edit their application materials. For hiring teams, this raises a practical question: does it matter?
The question is not just technical (can you detect it?) but strategic (should you act on the detection?) and legal (are there risks?). All three matter before you deploy AI detection in a hiring workflow.
A high AI score on a cover letter tells you one specific thing: the writing shows statistical and pattern-level characteristics of AI-generated text. It does not tell you:
What it might legitimately signal, in combination with other evidence: whether the written application reflects the candidate's actual communication ability, or whether it reflects the communication ability of a language model.
There are roles where the cover letter is genuinely a work sample, not just a formality. For those roles, AI detection is a reasonable tool:
Writing-intensive roles
Content writers, copywriters, communications managers, journalists, lawyers drafting client work. For these roles, the cover letter is the first work sample. A high AI score is relevant information about whether the sample reflects the candidate's ability.
Take-home writing prompts
When you explicitly ask for an original written response as part of the application (not just a cover letter), AI detection can flag responses that are likely generated rather than original. This is the clearest legitimate use case.
Screening at high volume
For roles receiving thousands of applications, AI detection can flag applications for secondary human review, not for automatic rejection. This is a triage use, not a decision use.
False positives disproportionately affect non-native English speakers
Published research (Liang et al., 2023) found that AI detectors flag writing by non-native English speakers as AI-generated at significantly higher rates than native speaker writing. This is a documented bias: ESL writing patterns correlate with the statistical signatures that detectors use to identify AI text. Using AI detection to reject applications without accounting for this creates disparate impact that may raise legal risk under employment discrimination frameworks.
False positives happen even for native speakers
A 4% false positive rate sounds small. At 500 applicants per role, that is 20 legitimate candidates incorrectly flagged. If screening for AI writing becomes an automatic reject criterion, those candidates never get a fair review.
AI assistance exists on a spectrum
Using Grammarly is AI assistance. Using ChatGPT to fix a grammar error is AI assistance. Using Claude to draft the entire letter and submitting it unchanged is AI assistance. Detectors cannot distinguish between these cases. A binary "flag or pass" policy collapses a spectrum of behaviors into a single category.
Employment law in the U.S. does not yet explicitly address AI detection in hiring, but several frameworks create risk:
This is informational context, not legal advice. Consult employment counsel before implementing AI detection in hiring workflows.
If you decide detection is appropriate for your workflow, these practices reduce risk and improve accuracy:
Use it for flagging, not for automatic rejection
A high AI score routes an application for additional human review. It does not remove the application from consideration automatically. A human reviewer makes the final decision, with the AI score as one data point.
Set a threshold and document it
Decide in advance what score triggers review (85%+ is a reasonable starting point for clear AI content). Document the policy so it is applied consistently. Inconsistent application creates its own legal and ethical problems.
Tell applicants what you are screening for
If your application process uses AI detection tools, say so in the job posting or application instructions. This is good practice and increasingly a legal expectation in jurisdictions that require transparency in automated decision tools.
Test your detector on your candidate pool
Run 50 known-human applications through the detector before deployment. If the false positive rate on your candidate pool exceeds the published baseline, adjust your threshold or reconsider the tool.
Do not use detection for resumes (only for writing samples)
Resumes are structured data: job titles, dates, company names. They score inconsistently on AI detectors and the results are not meaningful. Detection is most valid on open-ended prose responses.
The more durable signal than AI detection is in-person or live assessment of the skill that matters. For writing roles, a short timed writing exercise during the interview is more reliable than screening cover letters (AI-written or not). For analytical roles, a case study in the interview tells you far more than whether the cover letter was polished by ChatGPT.
AI detection in hiring is a narrow tool. Used as a triage layer for writing-specific roles, with transparency, consistent thresholds, and human review in the loop, it can add signal. Used as an automatic filter without these safeguards, it adds risk with limited benefit.
Airno runs text through seven detectors and returns per-method scores so you can see how strong the signal is. For hiring workflows, the per-detector breakdown matters: a document where all seven detectors agree is a different situation from one where only two fire.