---
title: "Applying ethics to AI decisions"
course: "PHIL 110 · Make ethical calls about AI"
unit: "AI ethics in practice"
lesson: 19 of 40
source: https://open.maestrocollege.edu/make-ethical-calls-about-ai/applying-ethics-to-ai-decisions
publisher: Maestro College
---

# Applying ethics to AI decisions

Lesson 19 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
- Apply at least one ethical framework from Unit 2 to an AI decision context — identifying what the framework reveals about the AI system's design, deployment, or outcomes that a purely operational view would miss.
- Identify at least two ways AI-specific characteristics — opacity, scale, and embedded optimization choices — create ethical risks that are qualitatively different from those in human decisions.
- Articulate why the ethical frameworks built in Unit 2 remain the right tools for evaluating AI decisions, even when the decision-maker is not human.
- Identify the five AI ethics components — bias, fairness, privacy, transparency, and accountability — as an integrated checklist for evaluating any AI system, and recognize that weaknesses in one component typically affect the others.

## Teaching notes
This lesson introduces the AI ethics components (bias, fairness, privacy, transparency, accountability) as step 5 of the ethical evaluation workflow — the workflow now extends to AI-specific evaluation. Students build this step across the next five lessons.

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