--- title: "AI and Fairness in Decisions" description: "Whether AI should make important decisions about people: jobs, loans, justice; who is responsible when AI makes unfair decisions; introduction to algorithmic fairness" 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: 2026-10-19 --- > **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`. # AI and Fairness in Decisions Whether AI should make important decisions about people: jobs, loans, justice; who is responsible when AI makes unfair decisions; introduction to 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 AI now helps make some very big decisions about real people, not just recommending videos or sorting photos. It can help a company decide who gets called for a job interview, help a bank decide who gets a loan, or help a doctor spot a warning sign in a scan. These are called high-stakes decisions, because getting them wrong can really hurt someone's life. **Example.** A company sorts 100 job applications from two equally qualified groups. The AI invites 80 out of 100 from Group A to interview, but only 40 out of 100 from Group B. Nothing about their actual skills was different, so this gap is a warning sign that the AI learned an unfair pattern. Bias like this can sneak in when an AI copies unfair patterns from old, biased data. That is exactly why letting an AI decide something about a person is risky: an unfair pattern can repeat itself thousands of times before anyone notices. **Tip.** Because the AI cannot understand fairness, people stay responsible: the company that chooses to use it, the engineers who built and tested it, and the rules that require someone to check its decisions. **Recap.** AI can help with big decisions about people, but when it is biased, humans, not the program, must stay responsible for fixing it. ## 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) - [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)