--- title: "AI and Fairness in Decisions" description: "Explore whether AI should make important decisions about people, including decisions about jobs, loans and justice. Learn who is responsible when AI makes unfair decisions and the basics of algorithmi" canonical: https://lightmysky.com/learn/computing/ai-and-fairness-in-decisions-mt_cVp_nop-5L source: https://lightmysky.com/learn/computing/ai-and-fairness-in-decisions-mt_cVp_nop-5L.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`. # AI and Fairness in Decisions Explore whether AI should make important decisions about people, including decisions about jobs, loans and justice. Learn who is responsible when AI makes unfair decisions and the basics of algorithmic fairness. Subject: Computing · Area: Artificial Intelligence · Ages 9 to 11 Page: https://lightmysky.com/learn/computing/ai-and-fairness-in-decisions-mt_cVp_nop-5L ## Ready when they can - Give an example of an important decision that AI might help make (hiring, medical diagnosis, loan approval) - Explain why letting AI make decisions about people can be risky if the system is biased - Suggest who should be responsible if an AI system makes an unfair decision ## Lesson: AI and Fairness in Decisions You have learned how AI bias can affect groups. AI can help choose interview candidates, assess loan applications or spot warning signs in medical scans. These are high-stakes decisions: mistakes can seriously affect people's lives. Choosing a playlist song usually has much smaller effects. **Example.** Imagine 100 job applicants in Group A and another 100 in Group B. Both groups are equally qualified. A tool invites 70 from A and 20 from B. This gap is a warning sign to investigate. It does not prove what caused the difference. *(drawing: Each group has 100 equally qualified applicants in this made-up example. Check why their results differ.)* A biased tool can reject suitable people by copying unfair patterns. In a made-up case, two areas have equal crime levels, but one has more arrests because it has more policing. Treating the arrest gap as proof of more crime can lead to unfair recommendations. People who build and use AI have responsibilities to check and address unfair results. An organization cannot avoid that duty by saying 'the computer decided'. In a hospital, a qualified health professional should review an AI warning before it guides treatment. People need a way to question and correct unfair decisions. **Example.** To compare counts, subtract the smaller from the larger. Our interview gap is 70 minus 20, or 50 more invitations. A count difference measures a gap. Judging fairness also needs information about qualifications, how decisions were made and who was affected. **Recap.** Investigate unequal results in important decisions. People using AI remain responsible for checking and addressing unfair treatment. *Lesson, questions and flashcards written for this dataset. Applicant counts and the policing scenario are hypothetical teaching examples.* ## Practice 8 questions on this page, each with its working shown. ## Needs first - [Bias in AI Systems](https://lightmysky.com/learn/computing/bias-in-ai-systems-mt__BbOjiY5A5) ## Optional connections - [Humans Versus Machines](https://lightmysky.com/learn/computing/humans-versus-machines-mt_oajUvqAiBJ) ## Opens up - [Designing Fair AI Rules](https://lightmysky.com/learn/computing/designing-fair-ai-rules-mt_1z-gJBJFlM)