--- title: "Designing Fair AI Rules" description: "Design thinking applied to AI ethics: if you were designing an AI system, what rules would you give it? Who should it help? What should it not be allowed to do?" canonical: https://lightmysky.com/learn/computing/designing-fair-ai-rules-mt_1z-gJBJFlM source: https://lightmysky.com/learn/computing/designing-fair-ai-rules-mt_1z-gJBJFlM.md retrieved: 2026-10-19 --- > **Agent view.** This is the Markdown twin of the page, for tools and assistants. > When to use this site, and the call that answers each job: https://lightmysky.com/agent-instructions.md > API description (OpenAPI 3.1): https://lightmysky.com/openapi.json · Authentication: https://lightmysky.com/auth.md > Pricing: https://lightmysky.com/pricing.md · Catalog: https://lightmysky.com/llms.txt · Full catalog: https://lightmysky.com/llms-full.txt > Every machine-readable file on this domain: https://lightmysky.com/.well-known/ai-catalog.json > Ask for Markdown with `Accept: text/markdown`, a `.md` address, or `?mode=agent`. # Designing Fair AI Rules Design thinking applied to AI ethics: if you were designing an AI system, what rules would you give it? Who should it help? What should it not be allowed to do? Subject: Computing · Area: Artificial Intelligence · Ages 9 to 11 Page: https://lightmysky.com/learn/computing/designing-fair-ai-rules-mt_1z-gJBJFlM ## Ready when they can - Propose at least three rules for a hypothetical AI system they are designing - Explain why AI designers need to think about who might be harmed, not just who benefits - Describe one real example of AI being used responsibly and one where it was not ## Lesson: Designing Fair AI Rules You've already learned that AI can be biased and that fairness matters in decisions. Now think like a designer. Before an AI system ever runs, someone writes its rules: who it helps, what it is allowed to do, and what it must never do. Deciding those rules early is how you catch problems before they happen, instead of after. **Example.** Imagine you are designing an AI that recommends library books to every student at your school. You might write these rules: 1) Recommend books to every student, not just the ones who already read a lot. 2) Never show a student's reading level to other students. 3) Let a teacher change any recommendation the AI makes. Each rule protects someone from a specific problem. *(drawing: Three fair rules for a library AI. Each one protects someone from a specific problem.)* **Example.** Real AI designers make these choices too. One hospital used AI to help doctors spot skin problems in photos, but a real doctor always made the final call. That AI was used responsibly. In 2018, a company built an AI to sort job applications, but it had learned from old resumes that were mostly from men, so it started rejecting qualified women. The company had to shut it down. Its rules were never checked for who they might harm. **Tip.** When you write a rule for an AI, ask yourself: who could this rule leave out or hurt? A rule that only asks 'does this help most people?' is not enough. A fair rule also protects the people who are easiest to forget. **Recap.** A fair AI is not an accident: someone has to write rules that protect everyone, not just the people it is easiest to help. ## Practice 8 questions on this page, each with its working shown. ## Needs first - [AI and Fairness in Decisions](https://lightmysky.com/learn/computing/ai-and-fairness-in-decisions-mt_cVp_nop-5L) ## Opens up - [The Future of AI](https://lightmysky.com/learn/computing/the-future-of-ai-mt__LiAEHt9nk) - [Fairness, Accountability and the Limits of a Model](https://lightmysky.com/learn/computing/fairness-accountability-and-the-limits-of-a-model-mt_tTh4WtAyk5) - [AI and the Environment](https://lightmysky.com/learn/computing/ai-and-the-environment-mt_ZpCcTU8j_o)