--- title: "Designing Fair AI Rules" description: "Apply design thinking 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: 2026-11-12 --- > **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 Apply design thinking 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 a real example of checking AI for unfair results and a real example of an unfair AI result ## Lesson: Designing Fair AI Rules You have learned that people are responsible for checking important AI decisions. Designers set goals and limits: who a system should help, what it may do and what it must not do. Writing rules is a start. People must also test how the system behaves and address problems. **Example.** Imagine a school book helper. Three rules could be: offer useful books to every student; keep each student's reading level private; let a teacher check and change recommendations. These address access, privacy and mistakes. Helping every student does not mean suggesting the same book to everyone. *(drawing: Check each protection. Following one rule does not prove the system follows the others.)* Ask who benefits and who could be missed or harmed. A benefit count does not answer both questions. Percent means out of 100. If 90 percent benefit, that is 90 out of 100. Find out what happens to the other 10. Ask affected people about their needs, then test and revise the rules. **Example.** Joy Buolamwini and Timnit Gebru tested commercial systems that assigned gender labels to face photos. They compared accuracy across groups and found unequal results. Checking for such differences is a responsible step. It does not prove that every use of a tested system is fair. **Example.** A Khan Academy account describes an experimental Amazon hiring tool. It lowered scores for some references to women, including 'women's' in a club name. The tool had learned from past resume data. This is an example of an unfair scoring pattern. The account does not establish that the tool rejected job applicants. Choose tests that check the behavior a rule requires. To test usefulness, include different needs and ask users how well suggestions fit. For example, if a school helper must accept typed questions, type a question and check its reply. Record the result, repair any problem and repeat the test. **Recap.** Write rules for access, privacy and correction. Check who could be harmed, test what happens and revise the design. *Text written for this dataset. Real examples summarize research by Joy Buolamwini and Timnit Gebru and a reported Amazon hiring-tool experiment described by Khan Academy. School-helper scenarios and benefit counts are hypothetical.* ## 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)