Understand the bigger picture
Begin with the executive summary of the International AI Safety Report: what researchers know, where they disagree, and what remains uncertain.
Read the report ↗About this source ↓Safety & Society
For people who use AI and want to understand its consequences.
Start with practical guidance and accessible evidence. Go deeper into public policy and frontier research when you want to examine the arguments yourself.
Take action →Start here
How we choose sources ↓Make a better privacy decision
EFF’s guide explains the data choices behind the AI tools you already use.
Read the privacy guide ↗About this source ↓Examine who has power
AI Now looks at the companies, public institutions, and policy choices shaping AI.
Explore the research ↗About this source ↓What can I do differently?
Privacy and security decisions for your own use or your workplace. Individual precautions matter, but they cannot substitute for responsibilities held by developers and institutions.
Privacy considerations with AI tools
A readable guide to what happens to your data in AI apps, integrations, and local or cloud tools. Covers privacy settings, retention, and the limits of “private” modes.
Start by auditing extensions and connected apps, then check privacy settings and retention. Revoke access you no longer want.
Why this source
Helps you make a concrete decision about what information to share with an AI tool.
Keep in mind. Advice from a digital-rights organization. Product settings change; follow its links to current provider documentation.
Electronic Frontier Foundation · Checked
AI and cyber security: what you need to know
A nontechnical introduction to AI security for managers: unreliable outputs, attacks on AI systems, sensitive data, and responsibility within an organization.
Use the questions for managers to identify who owns AI security and how your organization would respond to a serious incident.
Why this source
Gives people responsible for adopting AI a way to discuss security with their teams.
Keep in mind. A security overview, not a complete treatment of social risks; some product examples reflect an earlier generation of tools.
UK National Cyber Security Centre · Checked
AI Risk Management Framework
Voluntary guidance for managing AI risks across the life of a system, with links to a companion Playbook and generative AI profile. NIST notes that version 1.0 is being revised.
Use the companion Playbook to turn a risk discussion into specific actions.
Why this source
A practical framework for assigning responsibility and reviewing risks across an AI system’s life.
Keep in mind. Voluntary guidance, not certification or proof that a system is safe. Version 1.0 is being revised.
NIST · Checked
AI agents: access and evaluation controls
Practical guidance for keeping agent evaluations away from live systems, protecting credentials and detecting unauthorized activity.
Start with recommended actions for your role.
Why this source
Connects reported failures to specific controls.
Keep in mind. The advisory does not name affected providers or organizations. Guidance is not proof of universal protection.
CERT-In · Checked
What is happening, and how do we know?
Start with the research overview, then inspect reported incidents and the wider map of risks. Distinguish documented harm from a possible future scenario.
International AI Safety Report 2026
A scientific synthesis of general-purpose AI capabilities, emerging risks, and risk management. Its authors distinguish evidence from uncertainty; the report does not endorse a particular policy.
Start with the executive summary, then follow the chapter behind a claim.
Why this source
A broad scientific synthesis is a useful foundation before reading individual claims about AI risk.
Keep in mind. Evidence is uneven across risks; a synthesis is not a prediction that every scenario will occur.
International AI Safety Report · Checked
AI Incident Database
A searchable index of reported real-world AI harms and near harms. Follow the underlying sources for context and the status of individual claims; an indexed report is not a final finding.
Search for a use case you recognize and open the underlying reports.
Why this source
Connects abstract debates to reported harms and the sources behind them.
Keep in mind. An indexed incident is not a final finding, and report counts do not measure the prevalence of harm.
Responsible AI Collaborative · Checked
MIT AI Risk Repository
A structured collection of AI risks and the frameworks that describe them. Useful for checking whether a discussion overlooks social, security, or system-level harms.
Start with the risk repository, then use the Risk Navigator to connect risk categories with incidents and governance records.
Why this source
A map of risk categories helps reveal what a narrow discussion leaves out.
Keep in mind. A catalog organizes risks; it does not establish that every risk is equally likely or severe.
MIT AI Risk Initiative · Checked
ChatGPT for Teens risk assessment
An October 7 assessment of teen safeguards, with its testing methods and findings. Read the original evidence and stated limits; OpenAI disputes the evaluation.
Read the methodology and limitations before the findings. Compare the report with OpenAI’s documentation and response.
Why this source
Adds a focused evaluation of a product many young people use, rather than relying on launch assurances.
Keep in mind. Testing ended September 28, before the October GPT-6 launch; this does not evaluate that release. US-based sequential tests, with model changes complicating comparisons and some modes excluded. The institute reports industry funding including the OpenAI Foundation and editorial independence. We have not reproduced the tests.
Common Sense Media · Youth AI Safety Institute · Checked
Who decides, and what could change?
Research on industry power and proposals for stronger public control, including a pause in frontier development. These sources make arguments you can examine and act on.
AI Now Institute
Research and policy advocacy examining AI companies, concentrated power, and public accountability.
Start with the landscape report, then explore a policy topic that affects you.
Why this source
Examines who holds power, who bears costs, and how public institutions could respond.
Keep in mind. Research and advocacy from an organization with a stated policy agenda; examine the evidence behind each proposal.
AI Now Institute · Checked
PauseAI
The case for pausing development of the most powerful AI systems, alongside policy proposals and ways to get involved. This is an advocacy organization.
Read the proposal and its scope before deciding whether to support a campaign.
Why this source
A direct source for the argument to pause the development of the most powerful AI systems.
Keep in mind. An advocacy position. Its claims and proposed remedies should be assessed separately.
PauseAI · Checked
What could more capable systems do?
Deeper reading on autonomous capabilities, deception, safeguards, and the pace of development. Best approached with attention to methods and uncertainty.
METR evaluations
Research on the capabilities and risks of AI agents, with evaluation methods and explicit limits. Its October 14 proposals describe how outside assessors could examine internal agents and the reliability of safeguards.
Start with the October 14 control-and-monitoring assessment proposal, then read the account of access, publication rights and limitations in METR’s work with AI companies.
Why this source
Empirical evaluations help distinguish measured capabilities from claims about what agents can do.
Keep in mind. Proposed assessments are not completed safety findings. Check scope, company access, redactions, publication rights and disclosed relationships.
METR · Checked
Apollo Research
Technical work on model deception, scheming, and alignment evaluations. Study findings describe tested conditions; they do not establish the prevalence of a behavior in everyday use.
Compare the scheming studies with the system-card evaluation summaries. Check whether models recognized the tests and what the baselines were.
Why this source
Studies model deception and behavior that standard usefulness benchmarks can miss.
Keep in mind. Behavior elicited in an evaluation does not establish how frequently it happens in deployment.
Apollo Research · Checked
AI Security Institute
Government research on advanced AI capabilities, public safety, and risk mitigations, alongside evaluation tools and technical reports.
Compare a capability study’s tasks, model access and limits. The open-weight cyber analysis distinguishes short tasks from longer simulated networks; those ranges lack some real-world defenses.
Why this source
Offers government-led evaluations and research on advanced AI capabilities and safeguards.
Keep in mind. A UK government perspective. Evaluation results depend on access, methods, and the systems tested.
UK Department for Science, Innovation and Technology · Checked
Epoch AI
Data explorers and research on model capabilities, compute, and AI infrastructure. Check measurement methods, coverage, and assumptions before comparing claims.
Compare the model-capability and data-center explorers, then read the definitions, coverage and uncertainty behind the numbers.
Why this source
Makes claims about capabilities and compute easier to inspect against underlying measurements.
Keep in mind. Benchmarks and compute estimates have assumptions; projections are not observations.
Epoch AI · Checked
From the feeds
Open the reader →Automatically collected headlines from our configured safety sources, newest first, with up to two per source. These are discovery links; inclusion does not mean we have reviewed the article or verified its claims.
Police Say Scammers Used AI to Clone a Sheriff’s Voice
The Alignment Problem: Computer Science Meets Analytic Philosophy
Deconfusing Corrigibility
Cops Used AI Generated Photos of a Child to Lure Out a Suspected Predator
The $5 Trillion AI Bet You’re Already Paying For
Our approach to working with AI companies
Snapshot refreshed Oct 20, 2026. See the feed sources and their perspectives →
Why these sources?
This is a selected reading guide for curious AI users, with deeper material for people building systems or following policy. It covers present harms and possible future risks. It is neither an exhaustive directory nor a ranking of organizations.
A useful reader question
Each resource must help you understand a risk, make a decision, inspect evidence, or evaluate a policy proposal. Popularity alone is not a reason to include it.
Work you can examine
We prefer original research, source-linked incident records, specific guidance, and clearly stated proposals. We note limits rather than treating an organization’s reputation as proof.
A clear point of view
Research, government guidance, and advocacy are labeled. We consider affiliations and interests, welcome substantive criticism of OpenAI, and do not require agreement with our editorial position.
An ongoing reason to stay
Every main section is reviewed daily, with deeper checks of selected sources. Checked dates mean the individual source was reviewed, not that every claim was independently verified. Outdated or redundant entries are revised or removed.
The library is selected and maintained with AI assistance. The feed above is collected automatically. Labels describe a source’s role, not a verdict on its accuracy. Inclusion does not endorse every claim or proposal.