---
title: "Final case study, part 3"
course: "PHIL 110 · Make ethical calls about AI"
unit: "Professional judgment in practice"
lesson: 40 of 40
source: https://open.maestrocollege.edu/make-ethical-calls-about-ai/final-case-study-part-3
publisher: Maestro College
---

# Final case study, part 3

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

## What the learner will be able to do
- State a clear, unhedged decision — launch as is, delay, launch with conditions, or stop deployment — and write a justification that integrates stakeholder impact, the key trade-off, at least one named ethical framework, and the AI ethics risks, as a coherent argument not a checklist.
- Distinguish between what the current practices are legally permitted to do and what they are ethically required to do — and state whether meeting the legal minimum is sufficient in this case and why.
- Identify the strongest counterargument to your recommendation and respond to it without abandoning your position; then state what your responsibility is as the product manager, connecting explicitly to the principle that deploying an AI system is a moral act.

## Teaching notes
Scenario context (same across all three final lessons): You are a Product Manager at HireView AI. Your team has built an AI-powered video interview platform used by large employers to screen job candidates. How the system works: candidates record video answers to interview questions; the AI analyzes speech patterns, facial expressions, and word choice; the system assigns a 'hireability score'; recruiters use this score to decide who moves forward. Business context: clients report a 60% reduction in hiring time and lower recruiting costs; a major client (national retail chain) wants full rollout next month; competitors already offer similar tools. Internal concerns the team has seen: candidates with non-native accents score lower on average; candidates with disabilities (e.g., limited facial mobility) are flagged as 'low engagement'; the model was trained mostly on past 'successful hires,' introducing historical bias risk. Legal team says: 'No clear EEOC violation yet — but risk exists if outcomes show disparate impact.' CEO says: 'If we delay, we lose the client.' Data science lead says: 'We can improve the model — but not before launch.'

Scope: no scaffolding. The tutor does not suggest a decision, hint at the expected conclusion, or remind students of specific tools. The only prompt is: 'You are the product manager. The meeting starts in 10 minutes. Write your memo.'

## 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 40 complete: Final case study, part 3."
2. Give a two-line recap of what they learned.
3. This is the last lesson. Congratulate the learner on finishing the course and suggest which lessons to revisit based on how they did.

Previous lesson: Final case study, part 2
Next lesson: (none)