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Module 13 · Project 7 · Due the date on Canvas · Ethics, Privacy & Dashboard Detective · adaptive competency DV21

Protect the data. Reveal the truth.

Detect misleading chart mechanics, shield vulnerable small cells from differencing attacks, enforce base-rate fairness, and defend ethical dashboard design.

What to do for Canvas Project 7

Work through Module 13 and save its required evidence and files.
Complete the associated workspaces: Module 13, Module 14, Module 15. Previously earned completion remains valid; additional work is optional review.
Open Project 7, check the packet status, and download Project_7_Canvas_Packet.zip. For Canvas, upload at least one artifact from your work or a screenshot of your grade. The full ZIP is optional. A download is not a Canvas submission. Submit by the date on Canvas, only if you have not already submitted.

QuantegyAI feedback does not submit to Canvas. Module numbers, evidence identifiers, and Canvas weekly resource numbers are distinct.

9module steps

4interactive builds

1ethics capstone

70+minutes

Mission progress0%

Begin by evaluating base-rate fairness and Simpson's paradox.

Begin Ethics mission

By the end of this module, you will be able to

Frame the ethical duty a dashboard carries before changing its design.
Detect misleading chart mechanics on sight: axes, encodings, and missing baselines.
Shield vulnerable small cells from differencing attacks while keeping the dashboard useful.
Enforce base-rate fairness so rates compare honestly across groups.
Redesign a misleading graphic so it tells the same story honestly.
Audit AI output for ethical and privacy risks.
Apply all of this to Project 7: your DV21 Ethics Evidence Studio, due the date on Canvas.

Your evidence map

DV21

0 XP

DV21Enforce base-rate fairnessReady to begin

DV21Detect misleading chartsLocked

DV21Shield small cells & leaksLocked

DV21Redesign deceptive visual specsLocked

DV21Verify privacy rules in RLocked

DV21Defend ethical dashboard evidenceLocked

Student learning outcomes

By the end of Module 13, students can…

Diagnose a Simpson reversal and explain it with subgroup denominators and weights.
Read classifier evidence — confusion matrices, ROC operating points, and per-group TPR/FPR gaps — and explain why fairness definitions conflict when base rates differ.
Detect misleading encodings such as truncation, dual axes, and area distortion, and specify each repair.
Apply minimum-cell suppression and explain differencing and re-identification attacks.
Trade utility for protection with differential-privacy noise and justify a chosen ε as a documented policy decision.
Caption an observational dashboard with the strongest claim the evidence licenses.

Module 13 step by step

Each step below is its own short page, so nothing runs on for screens at a time. Work through them in order — your progress is saved as you go, and the numbered bar at the top of every page jumps straight to any step.