Exercises — Session 6 🟢 “AI and ethics”
Program : Applied AI — Beginner Level · Instructor: Yann Isola Material : a pencil, your phone (exercise 2), your critical thinking (all exercises)
Exercise 1 — Bias detective 🕵️
Duration : 20 mins · Format: alone or in pairs · Difficulty: ⭐⭐
Your mission: In each situation below, an AI produces a strange or unfair result. It's your turn to play detective: find where the bias comes from And suggest a remedy .
🔑 Reminder of the detective's magnifying glass: an AI learns data . When it goes off the rails, the first question is always: “What was (or wasn’t) in his training data? »
Case #1: soap distracts
An automatic soap dispenser (with sensor) works great for some people...but doesn't detect the hands of dark-skinned people.
- What is your hypothesis about the cause?
- What would need to change to correct the problem?
Case no. 2: the stereotypical translator
An automatic translator translates from a language where “doctor” and “nurse” have no gender. Systematic result: “ he is a doctor”, “ She is a nurse.
- Did the AI “decide” this? Where does this reflex come from?
- How could a translator present the translation more fairly? (Hint: some already do it — how?)
Case no. 3: the prudent bank… too cautious
A credit AI rejects applications from a certain neighborhood more often — even when people have a good salary. The AI never received the “neighborhood” information… but it knows the postal code.
- How was the AI able to “guess” the neighborhood when it was not given it directly?
- Why would this case be classified as “high risk” by the AI Act?
- What should the person refused be able to do? (Think transparency…)
Case No. 4: the image generator that lacks imagination
An AI is asked to generate “a photo of a CEO”. Out of 20 images, 19 show men in suits. We ask for “a person who does the housework”: almost only women.
- Where does this imbalance come from?
- Is it serious? Debate: “they’re just images” vs “images shape our ideas”.
🏆 Bonus question from the chief detective
Find something common to the 4 matters, and complete the golden rule: “An AI is never fairer than ________________. »
Corrected (do not read before searching!)
See the correction tracks
- Case 1: the sensor (and/or its calibration data) was mainly tested on light skin. Remedy: test and calibrate across the full diversity of real users, with diverse test teams.
- Case 2: the AI learned from millions of human texts where “doctor” is more often associated with the masculine gender. It reproduces the statistic, not a truth. Remedy: suggest both translations (“he/she is a doctor”) — some translators already do this.
- Case 3: the postal code is a indirect index (we say a “proxy variable”): it is correlated to the neighborhood, therefore sometimes to the social origin of the inhabitants. The AI finds these shortcuts on its own. High risk because of a major financial decision over a lifetime. The person must be able to obtain explanation and a human re-examination .
- Case 4: training images (web photos, image banks) reflect decades of stereotypes. This is serious on a large scale: these images in turn feed the web... which the next AI will use to learn. Vicious circle possible.
- Golden rule: “…than the data that fed it. »
Exercise 2 — Privacy audit of YOUR phone 🔍📱
Duration : 25 min (or at home) · Format: individual · Difficulty: ⭐⭐ Complement : the “Privacy Checker” tab of the session web page.
Today, the inspector is you — and the suspect is your own phone. Objective: discover what your apps know about you , and regain some control. Without panicking: we notice, we understand, we adjust.
Step 1 — Inventory (5 min)
List your 5 most used apps :
| App | Free? | How do you think she makes money? |
|---|---|---|
| 1. | Yes No | |
| 2. | Yes No | |
| 3. | Yes No | |
| 4. | Yes No | |
| 5. | Yes No |
Step 2 — The search for authorizations (10 min)
On your phone, open: Settings → Privacy (iPhone) or Settings → Security & Privacy → Permissions Manager (Android — exact path varies by brand ⚠).
For each of your 5 apps, check what it has access to:
| App | 📍 Stance | 📸 Camera | 🎤 Microphone | 👥 Contacts | 🖼️ Photos |
|---|---|---|---|---|---|
| 1. | |||||
| 2. | |||||
| 3. | |||||
| 4. | |||||
| 5. |
Step 3 — The interrogation (5 min)
For each box checked, ask THE detective's question:
“Does this app really NEED this access to do its job? »
- A maps app that asks for location: logic
- A puzzle game that requires your contacts and your microphone: 🤨 why?!
Write down your 2 most surprising discoveries:
Step 4 — Regaining control (5 min)
Choose at least 2 actions and really do them:
- Remove an unnecessary authorization (you can always put it back afterwards!)
- Change a position from “Always” to “Only when the app is used”
- Delete an app that you no longer use (it may still be collecting)
- Look at the “privacy policy” of an app for 2 minutes and note something understood… or not understood at all
- (Advanced) Find out how to request a copy of your data from a major service — it's your right of access GDPR !
Step 5 — The investigation report
Complete: “Before this audit, I thought my apps knew ______. When I checked, I discovered ______. I decided to ______. »
👨👩👧 For the youngest: do this audit with a parent — you will likely be the expert on the settings, and he/she will be as surprised as you. The GDPR also provides enhanced protection for minors.
Exercise 3 — The AI Tribunal ⚖️ (debate on ethical dilemma)
Duration : 20–30 mins · Format: whole group Difficulty: ⭐⭐⭐ Complement : the “Ethical Dilemmas” tab of the web page (voting and display of results).
The case to be judged
“Jules-Verne College wants to use AI to grade students’ papers and predict their chances of success. The court must decide: authorize, prohibit, or authorize subject to conditions. »
The roles
- ⚖️ Judge(s) : direct the debates, cut off time cheats, render a verdict motivated (the verdict must cite at least 2 arguments heard).
- 🟢 Defense (FOR the rating AI): you are arguing for college. Even if you think otherwise! Pleading an imposed position is strength training for the brain.
- 🔴 The accusation (AGAINST): you plead for the association of worried parents.
- 👥 The jury : listen, ask questions, vote at the end (on the web page or by show of hands).
The sequence (timed, it’s important!)
- Team preparation — 5 mins
- Defense pleadings — 2 mins
- Prosecution pleadings — 2 mins
- Right of reply (1 min each) — 2 mins
- Questions from the jury — 3 mins
- Jury vote + judges’ verdict — 3 mins
- Debrief all together — 3 mins
Help sheet — Defense 🟢 (to cut out)
Tracks (choose, expand, add your own):
- An AI note all students with the same criteria — no favoritism, no tired teachers on Sunday evenings.
- Rating faster → teachers have more time to help students.
- The prediction allows spot early a student who drops out and help him BEFORE he fails.
- We can impose guarantees : a human reviews each note, the AI is tested against bias.
Help sheet — Accusation 🔴 (to cut out)
Tracks:
- AI learns from old notes …which perhaps already contained injustices. The bias is copied, then amplified.
- Black box : how can you challenge your grade if no one can explain why?
- Student data is data of minors → maximum protection (GDPR!).
- A prediction can become a label: “the AI said you will fail”… what if we ended up believing it?
- The European AI Act classifies AI in education as high risk — it’s not a coincidence.
Questions the jury can ask (help)
- “Who will be responsible if the AI makes a mistake on a note in the patent? »
- “Did the parents give consent? And the students? »
- “What happens to a student with an original writing style that the AI has never seen? »
- “What specific guarantees do you offer? »
The debrief (the most important part!)
- Those who argued against their opinion: how did you feel? Did you find any good arguments anyway?
- Has anyone changed your mind during the trial? By what argument?
- Is there a solution median (authorize under conditions)? Which ?
- 🎬 Teacher's revelation: this affair has already happened in real life (notes by algorithm in the United Kingdom in 2020 → injustices → demonstrations → abandonment). What does that change to your verdict?
Additional cases (if the court wants to sit again)
- 📹 “Should facial recognition cameras be installed at the college entrance for security? »
- 🏥 “Should a village without a doctor equip itself with diagnostic AI that can be used without a doctor? »
- 💬 “Should we allow “virtual friends” chatbots for people who feel alone? »
- 🌍 “Should we limit the use of large generative AI for ecological reasons? »
⚖️ Moral of the court: On these questions, there is no ONE right answer hidden somewhere. There are arguments, values, possible guarantees — and citizens (you!) who will have to decide. This is exactly why we are debating it.