---
title: "Final case study, part 1"
course: "PHIL 110 · Make ethical calls about AI"
unit: "Professional judgment in practice"
lesson: 38 of 40
source: https://open.maestrocollege.edu/make-ethical-calls-about-ai/final-case-study-part-1
publisher: Maestro College
---

# Final case study, part 1

Lesson 38 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
- Identify at least two things that feel ethically problematic about the HireView scenario before any formal analysis — describe the unease in plain terms, not framework language.
- Name at least three stakeholders who might be affected by the platform going live, including at least one who has no voice in the launch decision.
- Articulate what decision is being pushed, who is pushing it, and what pressures — business, competitive, and relational — are driving the urgency.

## Teaching notes
Scope: this lesson comes before any analytical work — frameworks and the evaluation workflow are not introduced here. The student's task is to react as a professional, not to perform a structured exercise.

Scenario: 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 your 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.'

## 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 38 complete: Final case study, part 1."
2. Give a two-line recap of what they learned.
3. Name the next lesson, "Final case study, part 2" (lesson 39 of 40), and give the learner its link: https://open.maestrocollege.edu/make-ethical-calls-about-ai/final-case-study-part-2.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: Your toolkit and professional identity
Next lesson: Final case study, part 2