# Safety > Using AI safely, and judging the risk of letting an agent act. Page: https://schubergphilis.github.io/ai-training/safety/ License: CC BY-SA 4.0, https://creativecommons.org/licenses/by-sa/4.0/ This course is about using AI without getting hurt by it. It covers what you may share and how far to trust an answer, and what can go wrong once an agent is allowed to act on your behalf. ## Responsible use - [What may go into an AI tool](https://schubergphilis.github.io/ai-training/safety/responsible-use/): Where a prompt goes once you press enter, and which of the data you handle may follow it. How to try a new tool with dummy data, and when to say that AI helped. - [Redacting a document before you paste it](https://schubergphilis.github.io/ai-training/safety/redact-before-you-paste/): Take the names, numbers and addresses a task does not need out of a document before it goes into an AI tool, check that the task still works on what is left, and put the values back with a map that never left your computer. - [Saying that AI helped, and crediting what it copied](https://schubergphilis.github.io/ai-training/safety/saying-ai-helped/): Write the disclosure line in its three parts, what the AI did, what you checked and who is accountable, use a comparer that shows how much of a generated function came from a library, and look up the disclosure rule of the place the work goes to. ## Recognizing failure - [What an invented fact looks like](https://schubergphilis.github.io/ai-training/safety/spotting-hallucination/): Read a model's fluent summary of a report, predict which of its figures, quotes and references have no source, and check each one against the report with a checker on the page. - [Seeing bias across many answers](https://schubergphilis.github.io/ai-training/safety/bias-in-patterns/): Read two shortlists from a simulated ranker on the page that each look fair on their own, count who made the cut when the names are swapped, and decide how much checking a task that affects people gets. - [When the tool is usually right](https://schubergphilis.github.io/ai-training/safety/when-the-tool-is-usually-right/): Why attention to a tool's output decays fastest when the tool is good, what aviation and medicine found out about that, and how to build the check into the process rather than into willpower. ## Verifying outputs - [Checking habits that run when you are in a hurry](https://schubergphilis.github.io/ai-training/safety/checking-habits/): Find the row that does not add up in a table a chat assistant wrote, learn the four checks that run on everything that leaves your desk, and set how deep each one goes from the cost of being wrong. - [Following a claim back to its source](https://schubergphilis.github.io/ai-training/safety/following-the-source/): Walk two references in an AI-written paragraph back to the reports they name, find the one that does not exist and the one that says something else, and learn what to do with a claim you cannot check in time. - [Asking a model to disagree](https://schubergphilis.github.io/ai-training/safety/asking-a-model-to-disagree/): Why a model's yes to your plan is not a check, how to ask for the strongest counter-argument before asking for confirmation, and how to strip your own opinion out of the question when you want an assessment. - [Marking what a person has checked](https://schubergphilis.github.io/ai-training/safety/marking-what-a-person-checked/): Why an answer a person has checked and one nobody has checked look the same unless someone marks the difference, how CS50 marks the answers of its AI tutor, and how to put a light "checked by" line into a team's process and sample whether it gets filled in. - [Checking what an agent changed](https://schubergphilis.github.io/ai-training/safety/checking-what-an-agent-changed/): Compare what an agent did with what you asked for, find the deleted file and the sent message its summary left out, and put the one pause before the step that cannot be undone. ## Agent risk - [Why agent safety is different](https://schubergphilis.github.io/ai-training/safety/agent-risk/): What changes once an AI can act instead of only answer. How to decide what it may reach and do on its own, and where a person must approve first. - [Sizing an agent's blast radius](https://schubergphilis.github.io/ai-training/safety/sizing-the-blast-radius/): Read an agent's permission list as read, write and send rows, cut it down to the task, and put the one approval on the row that can't be undone. - [Tracing a planted instruction to the tool that leaks](https://schubergphilis.github.io/ai-training/safety/tracing-a-planted-instruction/): Follow one planted line on a web page through three permission sets, and find that the tool that can send is the one that leaks. ## Governance and oversight - [Writing an AI policy a team can apply](https://schubergphilis.github.io/ai-training/safety/writing-an-ai-policy/): Turn "use AI responsibly" into a one-page policy with allowed tasks, data, approvals, logging and an escalation route with names, using read and write access as two levels of sign-off. - [Reading agent logs and rerunning after a model change](https://schubergphilis.github.io/ai-training/safety/reading-agent-logs/): Reconstruct what an agent was asked, called and changed from its log, keep the log where the agent can't edit it with a named reader, and rerun a task set when the vendor changes the model. - [Assessing the risk of a use case before it starts](https://schubergphilis.github.io/ai-training/safety/assessing-a-use-case/): Rate three proposed AI use cases on impact and likelihood, find the ones at or above the threshold, and decide between extra controls and a written acceptance, with a record that can be reviewed. - [Introduction to the EU AI Act](https://schubergphilis.github.io/ai-training/safety/eu-ai-act/): What the European Union's AI Act asks of people who build and use AI at work. Whether a tool is an AI system, which risk tier a use falls in, whether you are provider or deployer, and which transparency duties touch everyday work.