10 free resources on this topic. Everything here is free and hand-picked. You can also search within this topic.
Chicago Booth Review on a study of 70,000 job applicants comparing machine screening with human recruiters.
Why I recommend it: Free article summarising academic research. Results come from one hiring setting and may not apply everywhere.
Peterson Technology Partners on AI-written resumes meeting AI screening, and what that does to hiring.
Why I recommend it: Written by a staffing firm that sells recruiting services.
Fabric's breakdown of how candidates cheat in AI-run interviews and how each method gets caught.
Why I recommend it: Fabric sells cheat detection. Its "83% would cheat" figure comes from the company.
Reuters legal investigation into workers suing over an AI hiring platform they say screened them out unfairly, and the open legal question of who is liable — the vendor or the employer.
Why I recommend it: Reuters sometimes shows a paywall after a few free articles; open it in a private window if you hit it. Directly relevant if you use or evaluate AI screening tools in hiring.
A September 2026 working paper by Brian Jabarian (Carnegie Mellon) and Luca Henkel (Erasmus Rotterdam) reporting a field experiment with 70,000 real applicants randomly assigned to be interviewed by a human recruiter or an AI voice agent. Applicants interviewed by AI were 12% more likely to receive an offer, with higher job starts and retention and no drop in on-the-job productivity. Transcript analysis traces the gain to more structured, consistent interviews that still adapt to each applicant.
Why I recommend it: If you have been told AI interviews are stacked against you, this is the largest piece of real evidence so far and it points the other way: consistent, structured questions helped candidates more than a tired recruiter on their eleventh call did. Prepare for them like any structured interview, with clear, specific answers.
Created by Brian Jabarian and Luca Henkel
Greenhouse argues that AI can absorb recruiting's volume but not its judgment, then walks through where recruiters should spend the time automation gives back - intake conversations, structured interviews, and candidate experience.
Why I recommend it: Read this from the other side of the table. Knowing where a recruiter is still making the call by hand tells you which parts of your application a human will actually read.
Plain-language overview of how AI screening tools evaluate applicants, their common failure modes, and the legal requirements now in force.
Why I recommend it: Read this before your next application. Understanding what the software looks for is not gaming the system, it is fair preparation.
Tracker of AI hiring regulations, enforcement actions, and bias-audit requirements affecting employers and candidates.
Why I recommend it: Worth bookmarking if you suspect an algorithm screened you out. Knowing the rules employers must follow gives you language to push back.
The September 2 Beige Book finds scarce AI engineers but weaker demand for some junior technology and administrative-support roles.
From the site: The September 2 Beige Book finds scarce AI engineers but weaker demand for some junior technology and administrative-support roles.
Why I recommend it: A useful data point showing that AI is reshaping hiring unevenly—senior AI talent is scarce while some entry-level demand softens.
Free tool that generates data deletion requests you can send to AI hiring and screening vendors holding your application data.
Why I recommend it: Your rejected applications can follow you through screening databases. This is a practical way to claw some of that back.
