Tendril
Loading…
New · guided experience
A curated walkthrough of the library — ordered lessons, a 15-question quiz between each, and 3 next-steps so you stay in flow. Earn XP, badges, and a streak as you go.
Lessons · 6434 available · Safety & Ethics view · disclosure
You are viewing safety & ethics lessons focused on disclosure. Use the tool lanes below to jump sideways into related workflows.
Drill down
Start with a real app or workflow. Each lane filters the library to practical lessons, not just broad theory.
541 lessons in safety & ethics
Search when you need it
Recommended next
Lessons handpicked for the disclosure shelf.
AI audits creator posts for missing or buried sponsorship disclosures before regulators or audiences notice.
AI deployment in workplaces raises consent questions that legal minimums don't fully address. Employers who lead on transparency gain trust; those who don't face backlash.
Model cards and transparency reports are how AI providers document what their systems can and can't do. Knowing how to read them — and what's missing — is a core deployer skill.
Publishing AI research or releasing models creates benefits and risks simultaneously. The norms for when to disclose, delay, or withhold are evolving — deployers need a framework.
Fresh disclosure lessons added to the library.
AI mental health tools must meet specific standards for disclosure, crisis handling, and clinical oversight. Vendor selection criteria matter.
AI audits creator posts for missing or buried sponsorship disclosures before regulators or audiences notice.
AI helps creators draft FTC-compliant paid promotion disclosure that survives a regulator's read.
AI can draft AI political ad disclosure language and on-screen labels, but the legal sufficiency of the disclosure is a campaign counsel question.
Subject tracks
AI audits creator posts for missing or buried sponsorship disclosures before regulators or audiences notice.
AI deployment in workplaces raises consent questions that legal minimums don't fully address. Employers who lead on transparency gain trust; those who don't face backlash.
Model cards and transparency reports are how AI providers document what their systems can and can't do. Knowing how to read them — and what's missing — is a core deployer skill.
Publishing AI research or releasing models creates benefits and risks simultaneously. The norms for when to disclose, delay, or withhold are evolving — deployers need a framework.
Effective AI red-teaming goes beyond clever prompts. The exercises that surface real risk include socio-technical scenarios, integration-point attacks, and post-deployment misuse patterns.
Some AI failures harm users and warrant public disclosure. Knowing when (and how) to disclose is its own discipline — far beyond the standard breach-notification playbook.
Watermarking AI-generated content is a partial solution to provenance. The current state is messy: standards are emerging, adoption is fragmented, removal is possible.
Your vendor's AI incident becomes your incident. Knowing your obligations to your own users — disclosure, remediation, credit — matters before the vendor's incident hits.
News organizations using AI for production, personalization, and translation face trust trade-offs. Disclosure and editorial judgment remain primary.
Federal and state laws now require AI disclosure in political advertising. Compliance evolves rapidly — and enforcement is ramping up.
Employees have evolving rights around workplace AI — disclosure, consent, opt-out. Compliance is operational necessity.
Customer consent for AI interactions is now legally required in many jurisdictions. Designing for meaningful consent matters.
Customer disclosure of AI involvement is now table stakes. Patterns that respect customers vs check legal box.
Public AI incident disclosure builds industry-wide learning. Done well, it shapes practice.
Draft an attribution policy that names AI contributions clearly, without using credit to obscure responsibility.
Stock-photo marketplaces selling AI-generated assets need provenance metadata, model disclosure, and indemnity terms that survive resale.
Newsrooms using AI for synthesis or translation need disclosure standards that maintain reader trust without burying every story in caveats.
AI can draft disclosure language for synthetic media, but organizational thresholds for what triggers a label require human policy judgment.