# Responsible use

> Using AI responsibly means knowing what may go into a prompt and what may not, respecting the confidentiality of other people's information, honouring the licenses of material you feed in and produce, and telling your audience when AI helped where they would expect to know.

Page: https://schubergphilis.github.io/ai-training/topics/safety/responsible-use/
License: CC BY-SA 4.0, https://creativecommons.org/licenses/by-sa/4.0/

*Safety* · topic `safety/responsible-use`

Using AI responsibly means knowing what may go into a prompt and what may not, respecting the confidentiality of other people's information, honouring the licenses of material you feed in and produce, and telling your audience when AI helped where they would expect to know.

## Concepts

- **Data privacy**: Personal data or source code pasted into a prompt leaves your control. Where it goes is set by the tool's terms: how long the provider retains it, whether it may train on it, where it is stored and where it is processed, and which subprocessors it passes through. A tool built on a gateway built on a model vendor has three such policies. Remove or replace names, contact details, health or financial information and anything covered by privacy law unless the tool is approved for it. [glossary](https://schubergphilis.github.io/ai-training/glossary/#data-privacy)
- **Confidentiality**: Information you are trusted to keep inside a boundary: customer contracts, unreleased plans, source code, colleagues' conversations. Pasting it into an AI tool is a disclosure to a third party, even if nobody reads it. The test is whether you would email the same text to that provider; if not, summarize, redact or use a sanctioned tool. [glossary](https://schubergphilis.github.io/ai-training/glossary/#confidentiality)
- **Licensing and attribution**: Material you give a model and material it produces both carry obligations. Feeding in copyrighted text or code you may not redistribute does not remove the restriction on it, and output that reproduces a source needs the same attribution the source demands. Keep track of where inputs came from and check that output which looks copied is credited or replaced. [glossary](https://schubergphilis.github.io/ai-training/glossary/#licensing-and-attribution)
- **Disclosure**: Saying that AI was used, to the extent the audience expects. A reader of a report, a reviewer of code or a party to a contract generally wants to know whether a machine drafted the text and who checked it. Disclosure is about trust, not shame: state what the AI did, what you did, and who is accountable for the result. [glossary](https://schubergphilis.github.io/ai-training/glossary/#disclosure)

## Links

- **Builds on:** [Capabilities and limits](https://schubergphilis.github.io/ai-training/topics/concepts/limits/)
- **Leads to:** nothing yet
- **Competencies drawing on it:** [Uses AI safely with confidential and personal data](https://schubergphilis.github.io/ai-training/competencies/safety/handles-data-safely/)

## Lessons

- [Redacting a document before you paste it](https://schubergphilis.github.io/ai-training/safety/redact-before-you-paste/) (tutorial)
- [What may go into an AI tool](https://schubergphilis.github.io/ai-training/safety/responsible-use/) (explanation)
- [Saying that AI helped, and crediting what it copied](https://schubergphilis.github.io/ai-training/safety/saying-ai-helped/) (tutorial)

## Sources

- `DLAI-2` [Generative AI for Everyone](https://www.deeplearning.ai/courses/generative-ai-for-everyone), DeepLearning.AI (course)
- `Academy ai-fluency-framework-foundations` [AI Fluency: Framework and foundations](https://academy.claude.com/courses/ai-fluency-framework-foundations), Claude Academy (course)
- `OpenAI Academy ai-foundations-dnq5w` [AI Foundations](https://academy.openai.com/public/courses/ai-foundations-dnq5w), OpenAI Academy (course)