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Micro-internship brief ≈5h in one sittinglow-code

Find a misleading chart in the wild — and rebuild it honestly

Misleading charts are everywhere — truncated axes, cherry-picked windows, 3D pie charts. Find a real one in news media or your feeds, dissect how it misleads with AI as your second pair of eyes, then rebuild it honestly.

Data AnalysisSpreadsheet AnalysisData Visualisation

Posted by The AI Internships

The work brief

  1. 01Hunt for a genuinely misleading chart in the wild: news sites, WhatsApp forwards, company presentations, political posts. Save the link/screenshot.
  2. 02Diagnose the tricks: name each technique (axis truncation, missing baseline, wrong chart type, cherry-picked dates). Then show the chart to AI and ask what you missed — keep only the accusations you can defend yourself.
  3. 03Get the underlying data — from the article’s own source, or reconstruct it from the chart’s labels.
  4. 04Rebuild the chart honestly and compare: what story does the honest version tell?

What you’ll produce

5 deliverables

Submission standard

Submit the source link, the original chart screenshot, your diagnosis, and your honest rebuild with the data you used. Include your AI workflow — tools, best prompts, and what you changed. Paste the actual data table behind your rebuild (with each value's source) so the honest chart can be reproduced.

  • Link to where the misleading chart appeared

    Public linkRequired

  • Screenshot of the original misleading chart

    Image uploadRequired

  • Your diagnosis: each misleading technique, named and explained

    Written responseRequired

  • Your honest rebuild of the same data

    Image uploadRequired

  • What the honest version shows vs. what the original implied (and the data you used)

    Include the actual data table you used to rebuild (values plus where each value came from), so the honest chart is reproducible.

    Written responseRequired

You’ll complete these inside your private workspace.

What you must submit as proof

This brief requires evidence an AI can’t fabricate.

  • The data behind your honest rebuild as CSV (values + source of each)

    The rebuilt chart must be reproducible from this data; the numbers must match the chart.

    CSV dataRequired

Submissions without this evidence cannot be submitted.

Protect other people in your proof. Blur faces, names, phone numbers and email addresses before you upload, and refer to anyone you worked with by role or number ("Listener 1", "the stall owner"). Your proof is only ever used to check your work — it is never published, never appears on your certificate, and is never shown in your public portfolio.

How your work is evaluated

The passing benchmark is 70/100.

Real find

14%

The chart is real, sourced, and genuinely misleading.

Diagnosis precision

29%

Techniques are correctly named and explained.

Honest rebuild

29%

The rebuild uses real data, right chart type, honest axes.

Rebuild ↔ data ↔ source

29%

The rebuild data must reconcile with the rebuilt chart and derive from the linked source or the original chart's labels. A rebuild whose numbers cannot be reproduced from the data, or with no real source, is a fail.

How we grade your AI usage

30% of your score

Using AI is the point — it’s the skill this certificate proves. You’ll answer three short questions about how you used it: what you asked, what was wrong with its first answer, and what you changed. Specific, honest answers score high. “I pasted the brief and submitted the answer” scores near zero.