---
title: "Bias in AI"
course: "PHIL 110 · Make ethical calls about AI"
unit: "AI ethics in practice"
lesson: 20 of 40
source: https://open.maestrocollege.edu/make-ethical-calls-about-ai/bias-in-ai
publisher: Maestro College
---

# Bias in AI

Lesson 20 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
- Identify the three main entry points for bias in AI systems: biased training data, biased design choices, and biased optimization targets.
- Recognize that bias can arise without malicious intent — well-intentioned systems trained on historical data reproduce historical inequity automatically.
- Apply bias identification to a given AI use case by naming the entry point, the affected group, and how the disadvantage arises.
- Identify the Gender Shades study (Buolamwini & Gebru, 2018) and Amazon's scrapped hiring algorithm as canonical examples of algorithmic bias — describe what each demonstrated and which entry point for bias each illustrates.

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

Case: Coded Bias (2021 documentary) — the first 10 minutes show Joy Buolamwini discovering that MIT's facial analysis software cannot detect her dark-skinned face. The documentary ends with congressional testimony, which previews the governance content in Unit 4.

## 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 20 complete: Bias in AI."
2. Give a two-line recap of what they learned.
3. Name the next lesson, "Fairness in AI" (lesson 21 of 40), and give the learner its link: https://open.maestrocollege.edu/make-ethical-calls-about-ai/fairness-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: Applying ethics to AI decisions
Next lesson: Fairness in AI