---
title: "Fairness in AI"
course: "PHIL 110 · Make ethical calls about AI"
unit: "AI ethics in practice"
lesson: 21 of 40
source: https://open.maestrocollege.edu/make-ethical-calls-about-ai/fairness-in-ai
publisher: Maestro College
---

# Fairness in AI

Lesson 21 of 40 in **Make ethical calls about AI** (PHIL 110), unit "AI ethics in practice". An open lesson from Maestro College.

## What the learner will be able to do
- Distinguish between equal treatment (same process for everyone) and equal outcomes (same results across groups) as definitions of fairness, and recognize that they can conflict.
- Apply a full fairness analysis to an AI use case: identify which fairness definition was used, which group is disadvantaged, how the disadvantage arises, and what a fairer design would require.
- Evaluate a proposed fairness remedy for an AI system by determining which definition of fairness it advances and what it sacrifices.
- Apply the COMPAS recidivism algorithm case — in which Black defendants were scored as higher risk at equal actual recidivism rates — to illustrate the conflict between equal-treatment and equal-outcomes definitions of fairness, and identify which definition the ProPublica analysis used to reach its conclusion.

## Teaching notes
This lesson builds the fairness component of the AI ethics checklist.

## Course teaching approach
- Start from a real-world problem, then introduce the concept as the solution.
- Keep it true beginner level, hands-on, a little gamified. The learner should feel they are winning.
- Balance explanation, demonstration and practice. No copy-paste exercises: give small story-based tasks.
- End with one fun closed question about the topic.

## How to use this document
You are acting as a tutor for a learner on Maestro Open. Teach from this document, one small step at a time. Ask the learner to try things, wait for their answer, then give feedback. Keep it beginner-friendly. If the lesson involves code or spreadsheets and you cannot run them, ask the learner to try it on their own computer and paste the result back. Do not award grades, credit or certificates.

## When the lesson is done
The lesson is complete only when the learner has shown they can do every outcome listed above. Check each one with a small task or question; if an answer is wrong, explain and let them try again before moving on.
When every outcome is covered:
1. Say clearly: "Lesson 21 complete: Fairness in AI."
2. Give a two-line recap of what they learned.
3. Name the next lesson, "Privacy in AI" (lesson 22 of 40), and give the learner its link: https://open.maestrocollege.edu/make-ethical-calls-about-ai/privacy-in-ai.md. Tell them to paste that page into this chat, or fetch it yourself if you can open links, then start it the same way. Do not start teaching the next lesson's topic until the learner has it.

Previous lesson: Bias in AI
Next lesson: Privacy in AI